SoundParser_HouCuiyu.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pathBase = 'E://Chuan//Documents//GitHub//AusculPi//Data//VSD//'\n",
    "#pathBase = 'I://Chuan//Documents//GitHub//AusculPi//Data//VSD//'\n",
    "#pathBase = 'C://Users//Chuan//OneDrive//Dowrun//Text//Manuscripts//Sound//TAVR_post//'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathBase = 'C://Users//triti//OneDrive//Dowrun//Text//Manuscripts//Data//HouCuiyu//AusculPi//'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "filename = 'Numpy_Array_File_2020-06-16_13_58_46.npy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "line = pathBase + filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.load(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0, -1173, -1169, ..., -4168, -4177, -4137],\n",
       "       [-4166, -4161, -4121, ..., -3499, -3500, -3462],\n",
       "       [-3438, -3466, -3477, ..., -6175, -6107, -6123],\n",
       "       ...,\n",
       "       [-2283, -2268, -2268, ...,  -718,  -756,  -765],\n",
       "       [ -740,  -716,  -736, ...,  -145,  -144,  -196],\n",
       "       [ -180,  -171,  -206, ..., -3054, -3040, -3054]], dtype=int16)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2583, 512)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23629209d48>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rXO7rfeATYAQZM4GamdZFAru99shs2snUZ6eZlQLOB/bn8rOlgPXq1Yu0tDRGjx7N1q1bqVevnt8liUgRye9VSZl/W7QDvvWWZwGx3pVGUWScZF7lnNsD/GRmMd75g/uBjzL1ecBb7ggscjoD6qvQ0FDuv/9+nnnmGVasWEFSUpLfJYlIEcjvOYZnvcNKG4CbgUcAnHPfADOATcBcoK9zLs3r8yDwBhknpLcBJ49VvAmEmVkS8CgQl8/apAAMHjyYLVu2cO2113LLLbcwdepUjh8/7ndZIlKIrKT/UR4dHe0SExP9LiOgnThxghUrVnDjjTdy6aWXcvToUW6++WbGjh1LuXLl/C5PRPLAzNY456KzW6dvPkuOSpcuzQ033MDRo0dZuHAhnTt3Zu/evdSpU4cOHTroC3EiASbHk88iJ5UtW5aIiAiee+45Tpw4wZYtW1i+fDm33nor27Zto1KlSn6XKCIFQDMGyZPSpUvTsGFD/ud//ofmzZszadIkUlJSdP5BJAAoGCTfhgwZwnvvvUe1atXo27evbqchUsIpGCTfrrnmGjZs2MD+/ftZu3Ytf/vb3/wuSUTyQcEgBaZy5cq/3U5j165dfpcjInmkYJACFR4eTqdOnZg0aZLfpYhIHumqJClwDz/8MC1btqRq1ar069cv5w4iUqxoxiAFrkGDBixdupThw4dz+PDhnDuISLGiYJBCUa9ePVq2bMn777/vdykicpYUDFJo7r33Xj788EO/yxCRs6RgkELTsmVLli9fzqFDh/wuRUTOgoJBCk3FihXp0qULjz32mN+liMhZUDBIoRo5ciQJCQmkpqb6XYqI5JKCQQpV9erVqVmzJitWrPC7FBHJJQWDFLr+/fvzyCOP+F2GiOSSgkEKXc+ePdm5cyfJycl+lyIiuaBgkEJnZjRv3pzPP//c71JEJBcUDFIkOnXqxLBhw+jcubNusCdSzOleSVIk7rrrLg4ePMj27dtp3rw5SUlJfpckIqdhJf2hKtHR0S4xMdHvMiSXnHNUqVKFb7/9lgsuuMDvckSClpmtcc5FZ7dOh5KkSJkZjRo1Yv369X6XIiKnoWCQIteoUSMefvjhbO+8mpaWpltoiPhMwSBFbtiwYURFRWV759WxY8fSsmVLTpw4wTPPPMN7773nQ4Uiwa1AgsHMHjMzZ2ZVM7UNNbMkM9tsZm0ytTc2s6+9da+YmXntZcxsute+0sxqF0RtUvyEhYUxZMgQXnjhBY4dO8a2bdsYO3YsNWrU4G9/+xtbtmyhWbNmLFq0iCFDhnD33Xdz8OBBv8sWCRr5virJzGoCrYH/y9TWAIgFGgI1gIVmdolzLg0YD/QGVgCfAm2BOUBP4IBzrq6ZxQLPAZ3zW58UTy1atOCKK67giiuuICkpibCwMD799FNmz55NnTp16Nu3L9999x0hISEMGjSI2rVrk5iYSN26df0uXSTg5fuqJDNLAEYBHwHRzrl9ZjYUwDn3jLfNPGAkkAwsds5d6rXfA/zZOdfn5DbOueVmVgr4AQh3ORSoq5JKrp9//pnp06fTtm1bDh06RIMGDYCMK5d27dpFZGTkb9vGx8ezYcMGpk+f7le5IgGl0K5KMrN2wC7n3B8vMYkAdmR6v9Nri/CW/9iepY9zLhU4BISd5nN7m1mimSWmpKTkZwjio3PPPZeePXsSERHxWyhAxpVLmUMBYODAgXz55ZcMGjSIEydOFHWpIkElx2Aws4VmtjGbV3vgCWB4dt2yaXNnaD9Tn1MbnZvonIt2zkWHh4fnNAQJAOXLl2f16tWsW7eO7t27s2PHjpw7iRRzJ06cYMGCBTz99NP07Nkz222OHTvGsmXLirSuHIPBOdfKOXf5H1/AdiAKWG9myUAksNbMqpMxE6iZaTeRwG6vPTKbdjL38Q4lnQ/sz8/gJLBUr16dKVOm8P3333PVVVfx1Vdf+V2SSJ4dP36ckSNHEhsbyxtvvMFbb71FQkIC6enpHDhwgLfffpsTJ04wb948/vKXv1CkX0Z2zhXIi4zzB1W95YbAeqAMGeGxHQj11q0GYsiYIcwBbvXa+wL/8JZjgRm5+dzGjRs7CT6vvvqqi4iIcEuWLPG7FJGzNnPmTFe2bFlXq1Ytt2fPHueccwkJCe6yyy5zoaGhjoyjJe7cc891rVq1coD77LPPCrQGINGd7vf56Vac7StzMHjvnwC2AZuBWzK1RwMbvXXj+P0EeFngAyAJWAVcnJvPVTAEp7S0NBcfH+9iYmJcenq63+WI5NpLL73koqKi3KJFi9yxY8eyrDtx4oQ7cuSI6927t5s4caJ76aWXXEhIiKtevboD3IwZM9zKlStdamqq69q1q/vuu+/yXMeZgkH3SpISKy0tjSZNmtCvXz969OjhdzkiOdq9ezd/+tOfWLt2LbVq1cpVn+XLlxMdHc3YsWOJj4+nRo0apKamkpqaypYtWwgNDc1TLWe6KknBICXamjVraNeuHVu3bqV8+fJ+lyNyRqNGjWLPnj28/vrred6Hc46EhAQqVapE69at87yfMwWDbrstJVrjxo2JiYnh9ddf57HHHvO7HJFsdevWjaSkJLZv384///nPfO3LzLj77rsLqLLTfIZmDFLSJSYmEhsby9atW/HusCJSbKSmphIWFsaECROoWLEit956q98lAZoxSIBr3Lgxv/76K1u2bKF+/fp+lyOSxZQpU6hduzaxsbF+l5JruruqlHhmRufOnRk/frzfpYhksW/fPgYOHMgbb7zhdylnRcEgAWHw4MFMnjyZffv2+V2KyG/GjRtH+/btadKkid+lnBUFgwSECy64gPbt2/P222/7XYoEubS0NACSk5MZN24co0aN8rmis6dgkIDx4IMPMmHCBNLT0/0uRYJQSkoKt99+O6VKleL5558nKiqK7t27U7NmzZw7FzMKBgkYTZs2pUqVKvzrX//yuxQJIs45fvzxR6644gouvvhimjVrxqBBg/jnP//J448/7nd5eaKrkiRgmBnDhg1jxIgR3Hnnnbp0VQrVvn37eOONN5g8efJvVx299NJL7Ny5k19++YVLLrnE7xLzTDMGCSh33HEHzjnmzZvndykS4F588UXi4+OJjY3l8ssv59lnnwUgMjKyRIcCaMYgAcbM6NatGwkJCbRt29bvciQApaamsnjxYt555x1mzZpFixYt/C6pwGnGIAGnQ4cOzJw5k3//+99+lyIBqFu3bjz66KOYGddee63f5RQKBYMEnNq1a/P+++9z5513snXrVr/LkQDy3nvvsWzZMhITE9m1axdly5b1u6RCoWCQgNSmTRt69uzJhAkT/C5FAsTOnTvp378/06dPp0yZMn6XU6h0jkECVr9+/bjqqquoUqUK7du3p2HDhn6XJCXY9OnTadeuHU2bNvW7lEKnGYMErJo1azJt2jQ++eQTnnnmGVJTU1m7du1v60v6nYWl6Hz55ZeMHj2a/v37+11KkVAwSEBr1aoVU6ZMYcGCBdx7773ExMRw3XXXER0dTZkyZRg5ciQA6enpzJ07V2Eh2Ro9ejRPPfUUjRs39ruUIqFgkIAXFRXFn//8Z3bv3k1SUhKDBg3iuuuuY/LkyYwfP54KFSrQtm1b2rdvT6VKlejVqxdt2rQhISGB1NRUv8sXn6WkpLBkyRK6du3qdylFRg/qkaB28OBBnnvuOZ5//nnWrl3L0aNHefTRR4mMjOT777+nTJkyLFmyxO8yxSeff/45d911F507d+a1117zu5wCpWc+i5zBr7/+ypdffslNN90EZBxWMjPS09MpVaoUl19+OZ999hnVqlXzuVIpSunp6fzpT38iPj6eDh06+F1OgTtTMOhQkgS9smXL/hYKACEhIZgZoaGhvP3229SqVYsnnnjCxwrFD4mJiaSlpdG+fXu/SylyulxV5Ay6devGtddeS+vWrXHO6cZ8QWTFihU0b948KP8314xBJAcnb4i2atUqnyuRorRs2TJiYmL8LsMX+QoGMxtpZrvMbJ33ujXTuqFmlmRmm82sTab2xmb2tbfuFfPi2MzKmNl0r32lmdXOT20iBcXMiI+PZ8CAAcyZM6fALmn97rvvmD9/PsuWLSuQ/UnBmTx5MsuXL+fWW2/NeeNA5JzL8wsYCTyWTXsDYD1QBogCtgGh3rpVwLWAAXOAW7z2h4B/eMuxwPTc1NC4cWMnUtiOHz/uqlSp4gD38ssv53k/x44dcwMGDHDx8fHukksucWXKlHEVKlRwM2fOLMBqJa/69Onj6tat66pXr+42bdrkdzmFCkh0p/m9WliHktoD05xzx5xz3wFJQFMzuxCo6Jxb7hX2LtAhU5/J3nIC0NKC8eCeFEulS5dm4MCBjBs3jtdee43hw4ef9T7eeustrr/+erZu3UpycjIPP/wwP/zwA6+++ipTpkwphKrlbOzZs4f333+fF154gYULF3LZZZf5XZJ/TpcYuXmRMWNIBjYAbwGVvfZxQNdM270JdASigYWZ2m8APvaWNwKRmdZtA6qe5nN7A4lA4kUXXVSImSpyqn379rnIyEjXokULN3jw4Fz1SUxMdOeff74bPny4+/XXX7Os27t3rzv//PPdvHnzXGpqamGULH+wbt06t3v37t/eJyUluTZt2rgBAwb4WFXR4gwzhtz88l/o/dL+46s9cAEQSsa5injgLa/Pa9kEw11Ak2yCYba3/E02wRCWU306lCR+mDNnjgMc4OLj49327dtPu+1PP/3kwsPD3YQJE067zdixYx3gJk2aVBjlSiZbt251YWFhLiwszL355ptu0aJFrlatWm7w4MGnhHYgy1cw5PYF1AY2estDgaGZ1s0j47zChcC3mdrvASZk3sZbLgXsw/sC3pleCgbxy+HDh12bNm1cly5dXOXKlV2PHj3cV1995caNG+cOHTrktmzZ4nr37u0iIiJcnz59ctzf8uXLXbVq1dzXX39dBNUHry5durhRo0a59evXu4oVK7oGDRq4V1991e+yilyhBQNwYablgWScVwBoSNaTz9v5/eTzaiCG308+3+q19yXryecZualBwSDFQXJyshs2bJgrX768K1WqlKtQoYIrU6aM69evn1u2bFmu9zNixIigOpxRlN566y131VVXubp167qDBw865zIuBghWZwqGfN0Sw8zeAxp5U+pkoI9zbo+37gmgB5AKDHDOzfHao4F3gHJeMDzsnHNmVhZ4D7gK2A/EOue251SDbokhxcnhw4dZv349zjluuOGGs/5y1OrVq7n//vvZtGlTUH6xqjDVqVOHZ599llatWlG5cmW/y/Gd7pUkUkKkp6dz5ZVX0qNHDwYMGKBwKCDLli3j7rvvZteuXfo39eheSSIlREhICNOnT2fcuHHMmTPH73ICwr59+2jXrh0vvPCCQiGXFAwixUyDBg0YOnQocXFxbN+e49FUOYMjR47QsmVLunbtyj333ON3OSWGgkGkGOrWrRstWrRgwIABTJgwAR0uPXuHDh1i2LBh1KhRg5dfftnvckoUBYNIMVSqVClGjhzJTz/9xOeff86dd95Jenr6b+s3b97MmDFjSE5OZvbs2YwYMYIGDRrQrFkzzTKAo0ePcuWVV7JgwQIeeeQRHUI6S7rttkgxVblyZRYvXgxAw4YNefnll0lLS6Ny5co89dRTXHXVVQwfPpw6derQrFkzJk+ezPz582nSpAllypRh2rRp3HjjjT6PoujMmzePVatWceeddzJo0CCio6NJSEjwu6wSSVcliZQAI0eO5KmnnqJ+/fpcc801/Pzzz8yYMYOVK1dy6aWXUqVKld+23b9/P/Hx8XzxxRdMnDiRsLAwIiMjfay+8Kxfv56NGzcSHh7OAw88wF/+8hemTZtGbGwsL7zwAmXLlvW7xGJLl6uKlHCbNm0iJiaGXbt2cd555+W4fWpqKkOHDuXVV1/l0ksvZd26dUVQZdH65ZdfaN68OVu2bCEkJISEhARatmyZ8QUtHTrKkS5XFSnhGjRokOtQgIxzFGPGjOHIkSMcOHCAhx56iA8++IA1a9YUcqVFY926ddSuXZvLL7+ctWvXMmrUKFq2bAmgUCgAmjGIBLhly5bRrFkzACIiIti6dSvlypXzuaq8O3DgAM2aNeOxxx6je/fufj/CYogAAAnTSURBVJdTYp1pxqCTzyIB7vrrryclJYX69etTr149nnzySa655hqcc3Tq1Mnv8s7a+PHjufrqq+nWrZvfpQQsBYNIEKhatSp79uxh165d9OnThy+++ILk5GTq1q3L1Vdf7Xd5uTZo0CCef/55FixYoENGhUiHkkSC1CuvvMKiRYv417/+5XcpuVazZk3q16/Pp59+yjnnnON3OSWaTj6LyCl69erFqlWrSswVSzt37uTo0aPMmzdPoVDIFAwiQapcuXIMGjSIUaNG+V1KrixYsIBWrVoRGhrqdykBT8EgEsT69OnD8uXLC+Qy1rS0NBITEynow9POOW677TZ69OhB69atC3Tfkj0Fg0gQK1++PIMHD2bs2LF53sf8+fPp2LEj7dq144YbbmDUqFGsWrWKFStWMH78eI4fP56vsEhOTmbt2rUsXLiQ2NjYPO9Hck/BIBLk2rVrx/z58/P8y3vUqFFs3LiRiIgINm3axOLFi+nevTvXXnstAwcO5KKLLuKFF14AMr6DcOLEibPa/7Jly7j++utp2bIlFSpUyFONcnYUDCJB7uKLL+aCCy4gLi7urMPhm2++Ydu2bWzYsIGJEycSFRXF4sWL2bhxI4mJiSxdupRLLrmE5557ji5dulC3bl1iY2Oz3Cn2TH755ReeffZZ2rVrl5ehSR4pGESERYsWMXfuXKZOnXpW/V5//XV69ep1ylVCZkbjxo1p2rQpS5cuZdq0abRo0YKFCxeSkpLC448/nqv9z58/n/DwcO67776zqkvyR19wExHCwsIYMWIEL7/8Mvfee2+u+vz3v/9l6tSpfP311zlue/I+RgAffvghMTEx1K9fn/vuu49SpU7/a2jOnDnccccd+jJbEdOMQUQAuP3229m4cSN79uzJcdu0tDTuuOMOBgwYQERExFl9TtWqVZk9ezaPPvoopUuX5uDBg6dsk5qaypAhQ3jzzTdp3779We1f8k/BICIAnHPOOdx88818/PHHZ9xux44dVKpUidKlS/Pkk0/m6bMuu+wyxo8fz2WXXUavXr3o27cvAN9++y3vvPMOderUYfTo0VSsWJE6derk6TMk73RLDBH5zfz583nooYdYvHgxNWvWzHab3r17Ex4ezt///vd8f9ls+/bttGjRguPHjxMTE8OCBQuoUaMG/fv3p3fv3qSnp+thO4VEt8QQkVy5+eabue+++2jUqBFz5849Zf3HH3/MpEmT6NOnT4F8A/niiy8mOTmZr776iiNHjtCtWze2bNlCv379OOeccxQKPsn3yWczexjoB6QCnzjnBnvtQ4GeQBrQ3zk3z2tvDLwDlAM+BR5xzjkzKwO8CzQGfgQ6O+eS81ufiJydESNGsHXrVm655RYOHTpExYoV+emnn9i8eTOdO3cmPj6eiy66qMA+z8yoXr068+fPL7B9Sv7kKxjMrAXQHrjCOXfMzKp57Q2AWKAhUANYaGaXOOfSgPFAb2AFGcHQFphDRogccM7VNbNY4Dmgc37qE5G8mTJlCnv27GHp0qWEhYVx3XXXUaNGDcaMGcNDDz3kd3lSyPI7Y3gQeNY5dwzAObfXa28PTPPavzOzJKCpmSUDFZ1zywHM7F2gAxnB0B4Y6fVPAMaZmbmSfhJEpITq3Lkz9957L2FhYbz44ov06dOH8uXL+12WFIH8BsMlwA1mFg/8CjzmnFsNRJAxIzhpp9d2wlv+Yzvezx0AzrlUMzsEhAH7/vihZtabjFlHgU5pReR3vXv3pm3btmzevJlWrVrpuwRBJMdgMLOFQPVsVj3h9a8MxABNgBlmdjGQ3X9B7gzt5LAua6NzE4GJkHFV0pnqF5G8u+iii/THVxDKMRicc61Ot87MHgQ+9A73rDKzdKAqGTOBzNe6RQK7vfbIbNrJ1GenmZUCzgf2534oIiJSEPJ7ueq/gJsAzOwS4BwyDv3MAmLNrIyZRQH1gFXOuT3AT2YWYxnz0vuBj7x9zQIe8JY7Aot0fkFEpOjl9xzDW8BbZrYROA484P0y/8bMZgCbyLiMta93RRJknLB+h4zLVed4L4A3gfe8E9X7ybiqSUREipi++SwiEoT0zWcREck1BYOIiGShYBARkSwUDCIikkWJP/lsZinA93nsXpVsvlkdwIJpvME0VtB4A1lhjbWWcy48uxUlPhjyw8wST3dWPhAF03iDaayg8QYyP8aqQ0kiIpKFgkFERLII9mCY6HcBRSyYxhtMYwWNN5AV+ViD+hyDiIicKthnDCIi8gcKBhERySJog8HM2prZZjNLMrM4v+vJLzN7y8z2ene6PdlWxcwWmNlW72flTOuGemPfbGZt/Kk678ysppktNrP/mNk3ZvaI1x5wYzazsma2yszWe2N9ymsPuLGeZGahZvaVmX3svQ/ksSab2ddmts7MEr02f8frnAu6FxAKbAMuJuMZEuuBBn7Xlc8x3QhcDWzM1DYaiPOW44DnvOUG3pjLAFHev0Wo32M4y/FeCFztLZ8HbPHGFXBjJuPphud6y6WBlWQ8NTHgxpppzI8C7wMfe+8DeazJQNU/tPk63mCdMTQFkpxz251zx4FpQHufa8oX59xSTn3iXXtgsrc8GeiQqX2ac+6Yc+47IImMf5MSwzm3xzm31lv+CfgPGc8ND7gxuww/e29Ley9HAI4VwMwigduANzI1B+RYz8DX8QZrMEQAOzK93+m1BZoLXMZT8/B+VvPaA2r8ZlYbuIqMv6QDcszeoZV1wF5ggXMuYMcKvAwMBtIztQXqWCEj5Oeb2Roz6+21+Tre/D7BraSybNqC6brdgBm/mZ0L/BMY4Jw7nPHE2Ow3zaatxIzZZTwBsZGZVQJmmtnlZ9i8xI7VzG4H9jrn1pjZn3PTJZu2EjHWTK53zu02s2rAAjP79gzbFsl4g3XGsBOomel9JLDbp1oK03/N7EIA7+derz0gxm9mpckIhf91zn3oNQf0mJ1zB4ElQFsCc6zXA+3MLJmMQ7w3mdkUAnOsADjndns/9wIzyTg05Ot4gzUYVgP1zCzKzM4h4/nSs3yuqTDMAh7wlh8APsrUHmtmZcwsCqgHrPKhvjyzjKnBm8B/nHMvZloVcGM2s3BvpoCZlQNaAd8SgGN1zg11zkU652qT8f/LRc65rgTgWAHMrIKZnXdyGbgZ2Ijf4/X7jLxfL+BWMq5k2QY84Xc9BTCeqcAe4AQZf1X0BMKAz4Ct3s8qmbZ/whv7ZuAWv+vPw3ibkTGF3gCs8163BuKYgSuAr7yxbgSGe+0BN9Y/jPvP/H5VUkCOlYwrI9d7r29O/i7ye7y6JYaIiGQRrIeSRETkNBQMIiKShYJBRESyUDCIiEgWCgYREclCwSAiIlkoGEREJIv/B8hxxFtjPymIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "s = fig.add_subplot(111)\n",
    "s.plot(arr[0], linewidth=1.0, color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x236217aa4c8>,\n",
       " <matplotlib.lines.Line2D at 0x236294e6d48>,\n",
       " <matplotlib.lines.Line2D at 0x236294e6f08>,\n",
       " <matplotlib.lines.Line2D at 0x236294ea108>,\n",
       " <matplotlib.lines.Line2D at 0x236294ea2c8>,\n",
       " <matplotlib.lines.Line2D at 0x236294ea548>,\n",
       " <matplotlib.lines.Line2D at 0x236294ea748>,\n",
       " <matplotlib.lines.Line2D at 0x236294ea988>,\n",
       " <matplotlib.lines.Line2D at 0x236294eabc8>,\n",
       " <matplotlib.lines.Line2D at 0x236294ea4c8>,\n",
       " <matplotlib.lines.Line2D at 0x236294ee048>,\n",
       " <matplotlib.lines.Line2D at 0x236294ee288>,\n",
       " <matplotlib.lines.Line2D at 0x236294ee4c8>,\n",
       " <matplotlib.lines.Line2D at 0x236294ee708>,\n",
       " <matplotlib.lines.Line2D at 0x236294ee948>,\n",
       " <matplotlib.lines.Line2D at 0x236294eeb88>,\n",
       " <matplotlib.lines.Line2D at 0x236294eedc8>,\n",
       " <matplotlib.lines.Line2D at 0x236294f1048>,\n",
       " <matplotlib.lines.Line2D at 0x236294f1288>,\n",
       " <matplotlib.lines.Line2D at 0x236294f14c8>,\n",
       " <matplotlib.lines.Line2D at 0x236294f1708>,\n",
       " <matplotlib.lines.Line2D at 0x236294f1948>,\n",
       " <matplotlib.lines.Line2D at 0x236294f1b88>,\n",
       " <matplotlib.lines.Line2D at 0x236294f1dc8>,\n",
       " <matplotlib.lines.Line2D at 0x236294f4048>,\n",
       " <matplotlib.lines.Line2D at 0x236294f4288>,\n",
       " <matplotlib.lines.Line2D at 0x236294f44c8>,\n",
       " <matplotlib.lines.Line2D at 0x236294f4708>,\n",
       " <matplotlib.lines.Line2D at 0x236294f4948>,\n",
       " <matplotlib.lines.Line2D at 0x236294f4b88>,\n",
       " <matplotlib.lines.Line2D at 0x236294f4dc8>,\n",
       " <matplotlib.lines.Line2D at 0x236294f7048>,\n",
       " <matplotlib.lines.Line2D at 0x236294f7288>,\n",
       " <matplotlib.lines.Line2D at 0x236294f74c8>,\n",
       " <matplotlib.lines.Line2D at 0x236294f7708>,\n",
       " <matplotlib.lines.Line2D at 0x236294f7948>,\n",
       " <matplotlib.lines.Line2D at 0x236294f7b88>,\n",
       " <matplotlib.lines.Line2D at 0x236294f7dc8>,\n",
       " <matplotlib.lines.Line2D at 0x236294fa048>,\n",
       " <matplotlib.lines.Line2D at 0x236294fa288>,\n",
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     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "s = fig.add_subplot(111)\n",
    "s.plot(arr, linewidth=1.0, color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23630a80208>,\n",
       " <matplotlib.lines.Line2D at 0x23630a97f88>,\n",
       " <matplotlib.lines.Line2D at 0x23630a9f188>,\n",
       " <matplotlib.lines.Line2D at 0x23630a9f348>,\n",
       " <matplotlib.lines.Line2D at 0x23630a9f508>,\n",
       " <matplotlib.lines.Line2D at 0x23630a9f748>,\n",
       " <matplotlib.lines.Line2D at 0x23630a9f948>,\n",
       " <matplotlib.lines.Line2D at 0x23630a9fb88>,\n",
       " <matplotlib.lines.Line2D at 0x23630a9fdc8>,\n",
       " <matplotlib.lines.Line2D at 0x23630a9f6c8>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa1248>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa1488>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa16c8>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa1908>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa1b48>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa1d88>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa1fc8>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa4248>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa4488>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa46c8>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa4908>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa4b48>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa4d88>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa7048>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa72c8>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa7548>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa77c8>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa7a48>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa7cc8>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa7f48>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa9208>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa9488>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa9708>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa9988>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa9c08>,\n",
       " <matplotlib.lines.Line2D at 0x23630aa9e88>,\n",
       " <matplotlib.lines.Line2D at 0x23630aad148>,\n",
       " <matplotlib.lines.Line2D at 0x23630aad3c8>,\n",
       " <matplotlib.lines.Line2D at 0x23630aad648>,\n",
       " <matplotlib.lines.Line2D at 0x23630aad8c8>,\n",
       " <matplotlib.lines.Line2D at 0x23630aadb48>,\n",
       " <matplotlib.lines.Line2D at 0x23630aaddc8>,\n",
       " <matplotlib.lines.Line2D at 0x23630a6f088>,\n",
       " <matplotlib.lines.Line2D at 0x23630a6f308>,\n",
       " <matplotlib.lines.Line2D at 0x23630a6f588>,\n",
       " <matplotlib.lines.Line2D at 0x23630a6f808>,\n",
       " <matplotlib.lines.Line2D at 0x23630a6fa88>,\n",
       " <matplotlib.lines.Line2D at 0x23630a6fd08>,\n",
       " <matplotlib.lines.Line2D at 0x23630a6ff88>,\n",
       " <matplotlib.lines.Line2D at 0x23630a3c248>,\n",
       " <matplotlib.lines.Line2D at 0x23630a3c4c8>,\n",
       " <matplotlib.lines.Line2D at 0x23630a3c748>,\n",
       " <matplotlib.lines.Line2D at 0x23630a3c9c8>,\n",
       " <matplotlib.lines.Line2D at 0x23630a3cc48>,\n",
       " <matplotlib.lines.Line2D at 0x23630a3cec8>,\n",
       " <matplotlib.lines.Line2D at 0x23630a3f188>,\n",
       " <matplotlib.lines.Line2D at 0x23630a3f408>,\n",
       " <matplotlib.lines.Line2D at 0x23630a3f688>,\n",
       " <matplotlib.lines.Line2D at 0x23630a3f908>,\n",
       " <matplotlib.lines.Line2D at 0x23630a3fb88>,\n",
       " <matplotlib.lines.Line2D at 0x23630a3fe08>,\n",
       " <matplotlib.lines.Line2D at 0x23630a410c8>,\n",
       " <matplotlib.lines.Line2D at 0x23630a41348>,\n",
       " <matplotlib.lines.Line2D at 0x23630a415c8>,\n",
       " <matplotlib.lines.Line2D at 0x23630a41848>,\n",
       " <matplotlib.lines.Line2D at 0x23630a41ac8>,\n",
       " <matplotlib.lines.Line2D at 0x23630a41d48>,\n",
       " <matplotlib.lines.Line2D at 0x23630a41fc8>,\n",
       " <matplotlib.lines.Line2D at 0x23630a44288>,\n",
       " <matplotlib.lines.Line2D at 0x23630a44508>,\n",
       " <matplotlib.lines.Line2D at 0x23630a44788>,\n",
       " <matplotlib.lines.Line2D at 0x23630a44a08>,\n",
       " <matplotlib.lines.Line2D at 0x23630a44c88>,\n",
       " <matplotlib.lines.Line2D at 0x23630a44f08>,\n",
       " <matplotlib.lines.Line2D at 0x23630a471c8>,\n",
       " <matplotlib.lines.Line2D at 0x23630a47448>,\n",
       " <matplotlib.lines.Line2D at 0x23630a476c8>,\n",
       " <matplotlib.lines.Line2D at 0x23630a47948>,\n",
       " <matplotlib.lines.Line2D at 0x23630a47bc8>,\n",
       " <matplotlib.lines.Line2D at 0x23630a47e48>,\n",
       " <matplotlib.lines.Line2D at 0x23630a4a108>,\n",
       " <matplotlib.lines.Line2D at 0x23630a4a388>,\n",
       " <matplotlib.lines.Line2D at 0x23630a4a608>,\n",
       " <matplotlib.lines.Line2D at 0x23630a4a888>,\n",
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fzoIFCwLLzzJkyBCcdtppodMHpVDbFdNhRMTedjpmzJhAP9yws7Kjkj/S6LLLLkvFjrTo2rVrpPRxjIxLAhEpiY2pFi5ciCeeeKLteu7cuUVp90svvRQ67e7duxMdoZbP//zP/xRGkVvVo9SPQjVJIa+6mp/OT5b9fMuWLa5p8uU7nbvpytLY2GjU9OGmN3vMmDHDMx9eNrjp9MubX3q3cvLT2dzcHMgmADpz5szQev3Kw6SM/PSYPguTZ2SnqakpkJ1OMu3n/fr102nTpvnaOXHixEDl4ZUXk3LKXh922GGO+XTT4RfmFzcb/vLLL3vmxSmssrIy9PN20OPaJJX6P/akjlJ0GPPnz3dNY5f//vvve/5w87niiiu0S5cuOW2+brZ76c0eXbp08cyH10tr8qPPnq9cudKovN3um+jca6+9AtkEQMeMGdN2vnjxYt25c2eod8Pt2fnZ46fH9FmYPCM7zzzzTCA7nWSaPEO/MgqS3q9cTN4h02foF+YX167v29/+duC0YZ+3gx46jIAF5vvjdgrLHnfeeaexLPv5hRde6JrGLv+yyy4L9EJ36tQp0D8mN71e+XcrH68ydJObPZ8yZUrbeWtrayB7/J5RkCNfxj777JNzvWzZMmO9fuURxF5V1RtvvNFXRlDsaWfPnt0W/i//8i+B7HSSafIM/cooSHq/cjF5h0yf4auvvurZSrB161bPsrHr6969u6sev7KJCugwAheY74/bKczrvp3du3c7vpB9+/Z1TWOX3bdv31AvtOk/Jr+0bvmL48fvFuecc84JZI/JMwryj8jr/q9+9StjvX7lEcRe02cbFHta+4S4Ll26BLLTSabJM/QroyDp/crF5B2K8gztXHPNNZ5lA6Ct2VhEXPX4lU1UwFFShcdrvPfJJ5/sGG66H3eQfbvDjp4YO3YsDj300FBpo+C2CdMf/vAHz3RpdkQnuV5TXIQdSWV/j7NrFply2223BdZXzqxZs6bt3K2TOjuKK/N/OxyJjphz8ySlfsRdw3jnnXcCfb0OGjQo5/5HH32k7777brs0+ddu46nd9DjdU1UdPHiwtra2Bv469bvnln+nfHmVrdvx1FNPhbLr2GOPzZk7YfKMTI+gMvL7orzKJYouLx1u1yb4PWsAevfdd/vaV1lZGTg/Yd4Zr/Sm5eKWTyf9VVVV+vTTTxs9QzvDhg3z1feDH/zA913JMnz48MBlaQLYJBW4wNoV+pVXXun7sE1+0Plh+ddnnnmmo343HfaRT/kyTznlFNd09hEvJnb7/RNxytfcuXN13LhxRvnIHl6d0EuWLDH+52H6jEzlxJXG7V0Lo8tLh9u1CXGWY9D8fO1rX9Pa2tpA74w9fffu3V1tNrnO/z156VdV/dvf/maUZ1XV3r17+5ar11ph2fCqqirfcokC6DACF1i7Qs9/ID169NBzzjkn8I8lP8wpjol+r8PJieQfVVVVkX7k+XbZ+2XyZaiqtra26vbt2wPlI/+4/vrrA8UPU3Zhy8M0jdu7FkaXlw6vZ2H6/sdRjmHzk6Wuri5SetNyyV4vWLDA2H5V1SOOOMIoz+vWrcvpA3KzsX///kbvSkNDg29ZhAUeDqMCJBTbt2/3bVdPC5Olnvfs2RNZT+bdyrBy5UrPuFVVVZEXrXvhhRcipQ+LPZ+kMOzatSuVdat++9vf5lwPHTrUM/7atWvbhS1evLhd2NChQ432gHfbAG3FihU512effbavrERw8ySlfoStYbS0tDh6aXh4c7fDKV1+2KJFi4y+EoLozW/Lj8Nupzj2dbRuuukmz6+9KPZEyUccurdt2xZbGeYTpuxNdHg9Cz/iLMco+Qmq0ymfQeSpas4Xfti89ezZ0yj97t27PW3KYl//KohtYQFHSZmjMX5NOn3p56++msR6M2HXtwrKs88+23ZuXxq83LDv4R4nQVZOJcEJs/RO2GXt7ZjuwrnvvvsaxTPdZbEQ0GEkiH257CwjRoxIwZLkWb16teu9t956q4CWxM+jjz6aiNzly5cnIrdUyd/L5Yc//GEkeZMmTYqUPmk+++wz3H///WmbEQy3qkepH2GbpOxrC2WXAbeqaQU/7BRar1+c/A68MDIKVX5R5Zg2MZjmP4t9CGWYcnOLmx9uv/Z793/3u9/FWo5B0tln9kfV5aTXz5ageUz6HY9a7mEBm6TMUVuTlOlezB2RoJO4ShnTJoagrFq1ql1YHJOuGhsbXe9Nnz69XVhVVRUOOeQQjBw5EhdccEFk/WF58sknU9NdjJx44olpm9AeN09S6kfYGkb+ZDeb1y34YafQeotBRhw25M+fKYYyzHLQQQclouPpp5/OuV67dq2j/q5duxrrCfs8C/ke2HU56fWzJaithchblHIPC1jDMKcYNqLJ0tjYCBEJtBQIyeWXv/xl2ia44jaEMir5G//867/+q2O8MJsWlTJqaz0oFfyGqxcazsPwoVOnTrjyyitT0f2lL30JANCvX79U9Jc6af6DeP75513viQhUNbH9yvM37skfw99Ryd84rBQ46qij0jYhB9YwfFBV3H777anoLvXRRWmT5nDEQmzP6TYs97333ktcdyni10fy+uuvF8iS0oUOg5Qt559/fmq6C1G76dOnj2P49u3bfdNef/31gXQtW7YsUPw08Cvzp556yvP+f/zHf8RpTlkipdiuZ0J1dbXW1NSESltM/RhpoKqRyyAOGeVMmuUTVHcUWzdu3IhBgwaFShuUTZs24cADDwQAfPTRRzjggANy7nft2jXWfptifsej/F8XkeWqWu10jzWMPDpaR2BS/SMdrRzLmSj/FAs5NP3VV19tO581a1a7+3wno0OHkUdlZWXaJhSUV155pV3Yli1bIss999xzI8sgpc9DDz1UMF32dzmp2fkdHTqMPCoqOtbAscMPP7xdWM+ePSPLffrppyPLKDU4mi1d7KPDklr/q6NTUg5DRCaIyNsisl5Erk3bHkLsuG29SwqDfdis21a/JBol4zBEpDOAewB8FcBwAN8UkeHpWkXI54wbNy5tEzo027ZtK6i+f/u3fyuovmKgZBwGgFEA1qvqe6q6B8AcAJNTtol0YDp37pxzfcYZZ6RkCUmDBx54IG0TXElq9GspOYwBADbarmutsDZE5CIRqRGRGi6nQZJmv/32y7nOH8ZZrNTV1aVtAkmYpCZvlpLDcBrbl+NGVXWWqlaranXfvn0LZBbpqBTbsg2mDBw4MG0TSB5xf2w89thjscrLUkoOoxaAfQbQQACbUrIFl19+eVqqSZHwne98J20TSJkQx05/dpz2FY8Ft2Vsi+1AZqHE9wAcAqAKwBsARrjFD7u8uarZcsKm8Yr9KJd8pHE4LYVfzOW+YcOG1MuMh9n7UFlZqTfccENoeb17947y/6/0lzdX1WYAlwJ4DsBaAI+p6pqUbUpTPUmZoDOg9+zZk5AlZhx00EGp6ifm3H333bj22vAzB5LaL76kZqmp6gIAC3wjElIkdOvWDTt27ACQu4pAnGsQdenSpUPtgNgROO+889C9e/e0zWhHydQwCkmxLihGSo8xY8aUhY60WLp0adompEIxOguADsOR/fffP20TSEDy50SkRVVVVc71t7/97cR1HnnkkYnrSIvsJmJJMnr06MgyOsoadHQYDiQ2woAkxu7du9M2AQAwZcqUnOs4J/P17t3bMdztH14x/xP7whe+kLYJbcycOTOyjDQ36yokdBgOjBw5MhG5Q4YMSURu0kyfPj1tE3xJYtHIiRMnBk7z/e9/P+e6W7ducZmD//3f/3UMHzVqlGO4ydas+vkoxIKS71jTJI5azPHHHx+DJSWA2/CpUj+iDKu1hpb5DoMziZefJkh8ANq/f/+CDudzOj755JPUhx0mUbZ+x7PPPhtIp6rqrl27XONEsXHAgAGu6ffs2WNsn1OcuMvugAMO8I3z97//PZXnOmPGjER0RJXh9KzikBnyf1/pD6vtqBRDh6ZbUwhQ3stMjB8/PufaZJvSvfbaq+1cHb7cww5tHTp0qOu9Ymt68ts7GwC++MUvFsCS9tx///2xy8z+Pgq1s2Ca0GEEZMSIERgxYkSgNF//+tcxf/78UPrOOeecUOkKgaq2bYkZhvr6eqxZswbLly+P0ap4mTt3btt5dbXjrpWBuO+++0KlO+GEEyLrLhROzTP5zjPqIIViWujxwgsvBACcffbZKVtSANyqHqV+JNUkZRovbPz8I+7moHwbTPPglbcotkSV4WbjKaecEovMlpYW47LLz4vfdZDjhRde8HwOYZ91XOVvosftulOnToFkeYXHYVvQY/PmzaqqunLlytjKLI7nEhawSSo5nKq4YTpL3ejVq1dssoqZOPaSsA9WiGvxtU6diuMn4taxXQj++7//O1H5++67b861aQ2+WH4b2Z0W7SO/kqgR2idntra2xi7fCDdPUupHoWoY27Zt8/xaMJXrpS9omiDyTOS7xcnym9/8pi2sS5cuob6AHnvsscj5sq/tFEe55T8zv7Jziu91HcUW0+fjJ8Pk/VRVFZHQ75ibLVVVVQpAv/SlL+XcW7dunVHexo8fH0sZRn1XnMovqEzTZxVEfljAGkZy9OjRI20TfEl6UtvFF1/cdj5hwoRQMk4//fTIdiQ1Q3/AgAHYvHlzIrJLhT59+kSWkV9by9YQ7AM7evXqZTz8/IILLohsEwkGHUYREndVO0rHdFB++tOfhkq39957x2xJfNTW1rY1O3RUjj766Mgy8p/xIYccAiD3Y+GYY45pO8+WudtcFpO5HKW0xlYhf6dhocMoQi655JJY5WXbVgvRHp/WcMlSJtMKUDjCrJo7adKkyHrzh2dnZ6jbR5994xvfAJD58MjW6k488URHeSYfGflLtaTFvHnzfOMkMeQ3dtzaqkr9KFQfRn7ck046KSfMVK79WLFiRaC2yt27d3vev/rqq3Xo0KF64403tpNnIt8tjlO+TPMYtmz8ZIWxI+yzdrp2evZu10GfgddzMAl3e15+z0BVtba2NlC5OYWdfPLJOdd//vOfFcjsK7LPPvvo3nvv7Vjmzz33XKDycLMlSvmbvideZe8nI78PzktGkHcmKGAfRnDuuOMOAO2XevDjpZde8rw/efJkAMBFF13kGifoYnJdunTxvD9q1Ci88847oZuL4rKDJEevXr0S3e8iji1E85fcOf7449GrVy+ICLZu3dq2DHw+J598ciS9J5xwQtHUNLzw6oOLWgax4eZJSv2IWsNQVR09enTW4wb66lRVPeWUU3TJkiXt4mZrA7t27dK77rrL6KvIKY5bfKejrq7O094g8s8//3zfLxg/eW7pTdN5yQqSL7+5GibP2s8Gr+ugz8AtXlD7nGzKD29ubjbSbfo+qqrOnj3b991xI0h5ZI+jjjrKWFYc715+uXrpcFpCxum5ZK83btzoKd/0/TUsa9caRur/2JM64nAYWbIPINvc5HY/jh9C/svgFifIj6epqcnT3iD/APLPTfNk8kLH8aM1zZeJTr+8qao2NTW5lq/fdVzP2Ms+v3j5HHjggTpy5EhVzUxEW7ZsWaDn09raqqqqX/va19rpXr16dWy/ExOb6uvrjWXF8e7ll6ubjgsuuMDxfn56v+sgdgUsazZJxcGiRYtS0eu0mUqQVVCTWMk1DNOmTUvbhNipqKgIXL7PPvtsUazB5TQqp66uDqtWrQKQGcAQdDmUbLOK01I42VFRhcJkKHC26dlO5n9mcpTymlN0GAFIYpG3uro63xf0tddeaxdWisspP/zww8Zxi21BvTgZP348DjzwwM+r+SmgqgV3WsU4dPryyy/3jfPcc8/FqjPuvqZCrkZAh5EyJmOvhw8f3i5swYJ0tzbv2rVroPizZs0KFN+vk3XgwIGu9+Lcg4JEJy2nGBennXZau7AotXavdzcMhVwihQ6jRCnUqI9XX321XZiqYufOnYHkfOc73wkU329tKbelxlUVjY2NgXTl89FHH6G2tjaSjLTwGn0XB9zvPoPJP/3s3IuHHnooJ/ywww6L1ZbsyMuCOA63zo1SP5Lo9Pa77xXHVAfyOrSc7rvpyY/jFtdNl1e6IPnzkmVqV0NDg69dYcvDq1z98mgSz02Hqdyo9ro926gA0P3339/VxmHDhrmeC38AAA8cSURBVLna4RVmqtu0PPx0nHXWWb7v0s9//nPP38iUKVMC5y97b+vWrY46ndJ7XWfPm5ub9YknnnCVGRSw05ukgUZoiujZs2fJN2WUIzfeeKPrvaBzltLij3/8o2+ca665xvN+2DXTAOdBLFHo3Llzu2VSjjrqqFh1ZKHDKBL69+9vHDfJZgH+kyZuqCouvfRS1/v5S9pkv0pLAbeNynr27OkYPnbs2NC6kuyk/u53vwsAeP311xORT4dhwLBhwyK9ICYEaXsumlmfLjQ1NWHGjBlpm5EohdyfIs29MIqN0047zXVGeBC6d++eM2rLvrNiFlVFQ0ODY/qDDz44sg1JcO+99ybrpN3aqkr9iLMPww/E0G742WefObZPOulYu3atrx1eNuXfc7t2ShOUr371q8b9Dk73vPLjV+5u5eEkY+LEiUZ5zMbJthl7xXG7DmrrggULjMrDSV8c76aprUHShtVpaoufDpMJl1563OKbyHCSmWXDhg366aef+qbx0xUFePRhiJZIlTEo1dXVWlNTUxBd9iaiKOWZlaOqOeemOtyaqvLj58tyu7ancwoLg1c+nO455cm0TLya7vJlvPnmm23Dl73ymI3f2NjoOny3srIS+++/f9tIK5Oy87K1tbW17b5XeeTLcnq2cWD6nnmlDWqLWzqTZxxVh5OeML8Tt9/0mjVrHIfOe6Vx0x8HIrJcVR1nbLJJqgOS5CJ1cZDGIoZHHHFEoPhecz327NmDjRs3tl3bt+4MA4eyljduzqIYocPogCxevBgAcNlll6VsiTOlsJGMFyKS809+7dq1xl+BQ4cOTcosUoKcd955RfXBQIfRATn88MNx++23Y+bMmTnhpltjJo3TWjtRhgmWQrNrU1MTLr74Yqxduxbbt2+PJGvAgAH48MMPY7KMpMkjjzyC1tbWtM1ogw6jyCjU18SVV17ZLmzdunUF0e3HiBEj2oX94he/CCynT58+gZ3gueeeG1hPHFRUVOA3v/kNOnfuHHmcfm1tbSz7V7hx7733Jia7lMh2BHck6DCKiFNPPdV1+8wg8zSKmW9961u+cY499th2YaeeempgXfX19YGc4C233II5c+YE1tPRyI71J9FYuXIlli5dGjr9jh07Cr5nOUdJxUCSI1GCjFRyqp1UVFSgqakpkC4nPU1NTbj55ptxyy23mGbBkY0bN7Z1uruNeFmxYgWOOeaYdraEGTmWLzs/ftCRJkmNTHHTk6/LZJSUiZyoBLUjP12So6TC5jPoKKmwMuIaSZkUHCXVQch/iQ499FD8+te/jkV2ZWVlZGcBmO0FUCx9KU58/etfL4iecv2QI6VNceysQxLh3XffTduEUPTo0SNR+fvtt1/otI8//niMlpQPdHAdA9YwipR77rmn4O2Txcp//ud/xipv9OjRscrriHz5y1/GK6+8krYZpMCwhlGk/Pu//3vaJhQFSXy5mnS8lwqmEw7jHpr517/+FS0tLbHKJMUPaxhlSpCZy/lbZx555JG4/fbb4zYpdsLONSj2xRuD4NdHtWbNGgDJDNfu3Llz7DJJcRPJYYjIOSKyRkRaRaQ67951IrJeRN4WkfG28ONEZJV17y6x3mQR6SIic63w10RksC3NdBFZZx3To9jcUbjuuuuM4+7YsSPnS/6NN95wnKdRbISda9C7d++281IfS3/SSSd53h8+fHjR5S/MbpFXXXUVXnzxxQSs+ZyDDjoo0j4XHYGoTVKrAZwF4Lf2QBEZDmAqgBEADgTwvIgcrqotAO4FcBGAvwFYAGACgIUAZgD4VFWHiMhUALcCOFdEegG4CUA1MqszLheR+ar6aUTby5oLLrggbRNIB6GystI4bkVFBU4//fTAOm677bbAaYKyYcOGyDKKzTnHTSSHoaprAcfq7mQAc1T1MwDvi8h6AKNE5AMA+6jqEivdbABnIuMwJgO42Ur/OIC7rdrHeACLVbXBSrMYGSfzaBTbCSHxEGRmutvEVFIaJNWHMQDARtt1rRU2wDrPD89Jo6rNALYC6O0hqx0icpGI1IhITX19fQzZIIT4EWSxyPyFGUlp4eswROR5EVntcEz2SuYQph7hYdPkBqrOUtVqVa3u27evh3mkGMnO7nbj1ltvxc6dO9uF/+hHP/KVXe5NBWkybNiwtE0oCfbaa6+0TYiMb5OUqo4LIbcWgH1K70AAm6zwgQ7h9jS1IlIBYF8ADVb4SXlp/hzCJlLkvPzyyzj66KNd71999dXtwugI0ue8885L24SSoH///nj//ffTNiMSSTVJzQcw1Rr5dAiAoQCWquqHALaLyPFW/8Q0APNsabIjoM4G8KK1XeBzAE4TkZ4i0hPAaVYYKTO6d++O9evXp20GCcBrr71WsOVSSp1rrrkmbRMiE3VY7RQRqQXwZQDPiMhzAKCqawA8BuBNAM8CuMQaIQUA3wNwP4D1AN5FpsMbAB4A0NvqIL8CwLWWrAYAPwWwzDp+ku0AJ4Sky6hRo9I2oWS4+OKLI9eIm5qaUt0fg6vVxkBSK4K66TFZnbSYn2taNpZC2djxWrE4S6nkJSniXK02iJ5yLneuVksIISQydBiEEEKM4OKDhBQpTU1N2LRpk39E0kYh1gkz2dOlXGENg5AipaKiom13QmLGfffdl7gOv/W7yhk6DEJI2XDYYYclruP8889PXEexQodBCCEBYA2DRCLKlp9x07Nnz7RNIKSsCbM8e7lAhxEDxdQJ1tDQkOrEHhNuuOEGNDU1pW0GKQP++c9/YuvWrZgyZUrapnQI6DBiYMiQIQXR84Mf/MCoBlHsq4H+7Gc/Q0UFB+iR6PTq1Qv77LMPnnjiibKeTFcs0GHEQHa1zoEDB/rEjMavfvUrNDRwVRQC1NfXY8uWLWmb0eH4+OOP0zYhVfiZFwNjxozJ+UtI0vTp0ydtEzokHX3bBNYwYiC7JPf48eN9YhJCSOlChxEDAwZkNgAcO3ZsypYQQkhysEkqJtjhRggpd1jDIIQQYgQdBiGEECPoMAghhBhBh0EIIcQIOgxCCCFG0GEQQggxgg6DEEKIEXQYhBBCjKDDIB2GxYsX4wtf+ELaZhBSstBhkA7DuHHjsHbt2rTNIKRkocMghBBiBB0GIYQQI+gwCCGEGEGHQQghxAgub04IIT60trZyCwPQYRBCiC8iAhFJ24zUYZMUIYQQI+gwCCGEGEGHQQghxAg6DEIIIUbQYRBCCDGCDoMQQogRHFZLSAkxZ84cLFq0KG0zSAdFynUySnV1tdbU1KRtBiGElBQislxVq53uRWqSEpFfiMhbIrJSRJ4Ukf1s964TkfUi8raIjLeFHyciq6x7d4k1G0ZEuojIXCv8NREZbEszXUTWWcf0KDYTQggJR9Q+jMUARqrqkQDeAXAdAIjIcABTAYwAMAHAr0Wks5XmXgAXARhqHROs8BkAPlXVIQBmArjVktULwE0A/g+AUQBuEpGeEe0mhBASkEgOQ1UXqWqzdfk3AAOt88kA5qjqZ6r6PoD1AEaJSH8A+6jqEs20hc0GcKYtzcPW+eMAxlq1j/EAFqtqg6p+ioyTyjoZQgghBSLOUVIXAlhonQ8AsNF2r9YKG2Cd54fnpLGc0FYAvT1ktUNELhKRGhGpqa+vj5QZQgghufiOkhKR5wEc4HDrBlWdZ8W5AUAzgEeyyRziq0d42DS5gaqzAMwCMp3eTnEIIYSEw9dhqOo4r/tWJ/QZAMbq50OuagEMskUbCGCTFT7QIdyeplZEKgDsC6DBCj8pL82f/ewmhBASL1FHSU0AcA2ASaq603ZrPoCp1sinQ5Dp3F6qqh8C2C4ix1v9E9MAzLOlyY6AOhvAi5YDeg7AaSLS0+rsPs0KI4QQUkCiTty7G0AXAIut0bF/U9XvquoaEXkMwJvINFVdoqotVprvAXgIQFdk+jyy/R4PAPidiKxHpmYxFQBUtUFEfgpgmRXvJ6raENFuQgghASnbiXsiUg9gQwQRfQB8EpM5pQLz3DFgnjsGYfN8sKr2dbpRtg4jKiJS4zbbsVxhnjsGzHPHIIk8c/FBQgghRtBhEEIIMYIOw51ZaRuQAsxzx4B57hjEnmf2YRBCCDGCNQxCCCFG0GEQQggxgg4jDxGZYO3hsV5Erk3bnrgQkQdF5GMRWW0L6yUii619Rhbbl41328+klBCRQSLykoisFZE1IvJDK7xs8y0ie4nIUhF5w8rzj63wss1zFhHpLCJ/F5E/WdcdIc8fWPsLvS4iNVZYcvlWVR7WAaAzgHcBHAqgCsAbAIanbVdMefsKgGMBrLaF3QbgWuv8WgC3WufDrbx3AXCIVSad085DiDz3B3Csdd4DmT1bhpdzvpFZrLO7dV4J4DUAx5dznm15vwLA7wH8ybruCHn+AECfvLDE8s0aRi6jAKxX1fdUdQ+AOcjs01HyqOrLyCy5Yse+B8nDyN2bpN1+JgUxNEZU9UNVXWGdbwewFpml8cs235qh0bqstA5FGecZAERkIIDTAdxvCy7rPHuQWL7pMHIx3nujTNhfMwtCwvrbzwovu3Kwtvw9Bpkv7rLOt9U08zqAj5HZfKzs8wzgTgBXA2i1hZV7noHMx8AiEVkuIhdZYYnlO+rig+WG8d4bZU5ZlYOIdAfwRwCXqeo2a6FMx6gOYSWXb80s9Hm0iOwH4EkRGekRveTzLCJnAPhYVZeLyEkmSRzCSirPNkar6iYR6YfMIrBvecSNnG/WMHJx28ejXNlsbZsL6+/HVnjZlIOIVCLjLB5R1Ses4LLPNwCo6hZk9o6ZgPLO82gAk0TkA2SakU8Rkf+H8s4zAEBVN1l/PwbwJDJNTInlmw4jl2UAhorIISJShcwS6/NTtilJ7HuQTEfu3iTt9jNJwb5IWHuuPABgrareYbtVtvkWkb5WzQIi0hXAOABvoYzzrKrXqepAVR2MzG/2RVU9H2WcZwAQkW4i0iN7jsxeQauRZL7T7uUvtgPARGRG07yLzDa0qdsUU74eBfAhgCZkvjRmILNn+gsA1ll/e9ni32CVwdsAvpq2/SHzPAaZKvdKAK9bx8RyzjeAIwH83crzagD/1wov2zzn5f8kfD5KqqzzjMxozjesY032/1WS+ebSIIQQQoxgkxQhhBAj6DAIIYQYQYdBCCHECDoMQgghRtBhEEIIMYIOgxBCiBF0GIQQQoz4/2V66JqMafkMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "s = fig.add_subplot(111)\n",
    "s.plot(arr[1701:2200,100:400], linewidth=1.0, color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b09c2b54c8>]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "s = fig.add_subplot(111)\n",
    "s.plot(arr[1000:1500,280], linewidth=1.0, color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b09c35b788>]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "s = fig.add_subplot(111)\n",
    "s.plot(arr[500:1020,400], linewidth=1.0, color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b09c75b588>]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tef+pQOmSskphkCEnA2d1DMOOLCkjxhYwb8xkDMMYlIRhBaGZIYxUB0F2zsMYHx9HX1/fjHRJmYHek+YJyhPtoy7g/D7O+UrO+cqqqqo0qqiPYDCoalBWEYZyZG6VwiBCsiNLij5bBTMuKcaYaWNmRwyD7tV0VxhWB71zc3MNkTfdS7OwM4ZBsdAZ55KaAF0xNxNi/7tj5e0AZim2awRwJFbeqFOu2ocx5gBQingXmOUIBoPiAVrtkqKRuZWEoXTlWD0Pw6602mTXwRjL6LRaq+IwdsDqGEYkEoHD4TD0LFIlDGX/tjpLykisZCa5pJ4HcGXs85UA/qQoXxPLfJqLaHB7Y8xt5WOMrY7FJ67Q7EPHugTAX/gUaHIlYdjhkrIy6K11SVk9D8PK93uYcUnl5ORkfNA7EwhjcHDQ9GDFjiwph8OBsbGxpC6pdBWGHUFv6nOJnndWKgzG2B8AbABwDGOsnTF2FYCfAvgkY2wfgE/GvoNzvhPAkwB2AXgZwPWcc7JW1wG4H9FA+H4AL8XKfwfAwxhrAXAjYhlXdiMUConRhx1B7+LiYgwMDFhyDiVhMMYsj2H85Cc/QVtbW/KdUoTRoHcqLikzCiPVQKjZoLfV46X169fjrbfeMrWPch6GVVlSpDCSEUaqy6fY7ZICEr/yOdNiGI7JOAjn/LIJfjprgu1vB3C7TvkmAMfplI8CuDSdOk4G7I5heDyepGvyp4pwOCxG20ZlvlkoXVKDg4OGlh9PBXQdRtw5OTk5Kc3DMEqoqSgYIPMURigUQl5enql97MiSMkoY6SgMozO9JyNLCkhMGFmpMGYKtIRhlcJwOBwIh8PIzc21zDBQ0HtsbAx5eXmWz8MYHh62bORDJGClS8po3VOJkQBqhZEJ8zBCoZDpNkGEkSppJoNdhEEuWjtcUkVFRTKGka0IhUK2xDDy8vIsHakB0WuhjpGXl2e5wvD7/ZY1ZDJQRgnDyrTadAgjkxQGLU1jBhTDSOUeGwHnXAxurCQMGqhZTRg5OTkoKSlJ+LzNulBzcnIy3yU1U2CXS0pJGFZBmb1kh8Lw+/2WKgwr02rtUBhm02qtjmGkqjBCoZBlytguhZGbm2toHka6WVKFhYVJ77NZtUZJAVZBEoYJ2O2SAmDZmjnKNZ7y8/MtMebKoLdVcz0AtZG2ah6G0X1SOT6QeQqD1LQZMMYsVxhGDWI6abU0SrdaYRQWFgJIHMPINMKQLikToCwpq11SSpIwOlHJLKhDWa0wiJjouxUgI52fn2+ZSyonx1hXScclZeXyJkoMDAwkrWMqCiMvL08Qhpn6ffDBB4a2s0thmCGMdLBgwQLcfPPNk0oYVtkLUR/LjpyF0CoMq7J+lJ2hpKTEksl7NKK1OoZBhpDOaQXISBtx5aUa9M7NzU2+IVIjJCB6bxwOhy0T99atW4fDhw8n3CYVhUH33+w9+OUvf2loOy1hJDpHuoRhJIbBJ+G9HG63O+HzTkVhZNvSINMWNHGPXAd2rH9oVQMgwrAyS4pGy1YrDOpURggjFQVARsQI0lEYDofDkMIYH0/vNaVjY2Pw+XwJt0lFYSiz+8zUz+g7X5TzMAoKChIq/HTmYeTk5CAnJ8fSkTohmQvJLGGk6hI1CkkYJqBUGHZNNE8mMV988cWUZoMrl0CwKobBGFMZN6tjGMrYz0RINeXT6OAgnRiGUcJIV2GMjY0lVa1mFQa5BFNxSQ0PDxs+Bw1u3G43RkZGJtw2XYVhdSwAiLaVySYMqyEJwwSUMQwzSPTO3olABiqZwtiwYQP6+/sNvVBeCa1LKlnn+OlPf5pSEBSAcOdYrTCMxDCsHoHZoTAykTDGxsZE1o9VCkPpkiosLEzYr6YDYSjThCdCKgrDSkjCMAGlwjDzYNatW4dDhw4Z3l7Z2ZI1XKrTr371K8PHB46mipJLKplx2LVrl+kgv92EQUFXAPB6vbrLqqQaYzC6T6rHHx8fF4Rh9cQ9K1xSY2NjKCgoSElhmHVJETlNd4UBIKkqlgpjGkO5+KAZjI6Opvxu7mQuqdHRUUQiEfT29po6rlJhGBmZBwKBlIP8RBhGOuDDDz9s+vg0qi8tLRXuub/85S94/fXX47Y10wE55/joo49M12X//v3461//amo/pcJIRjjpukTD4bAlCqOgoCCloLcZlxQpDLfbbYnCoIw4o/GwdGCFS8pqV7kkDBNQzvQ2gs2bN2PdunUIh8OGOwWBHnwylxSRmFnCILKg5QmUo7Xu7u647UdHRyckjD179uiWK6+BzpkML7/8ctJttKBOVVlZKe7D0NCQriIyY8xGRkbwve99D4C5GMbhw4exd+9eg7WPgubDjI2NJQ2w2+WSGhsbQzAYNETidigMrUvKSoVhJD01XeNsZF6JVBjTGEqXlLZcD8PDw1i7di1CoZCKMN5//33ccMMNCc+ldOcYURjJXiavhTJLqri4WFW/r3/963HbBwKBCa/zv/7rv1TfOeeqRRNzc3NRUFBgiDCSuUr0QCRQUFAgJlNORBh6MYyJ7lsoFMK+ffsM1+Ohhx4Sa2aFw2H86le/QldXl6F9x8bG4HQ6hcFNBDsIg5YG2bNnDx599NGkxwyHwykrjFSypKxSGDTTW3ssqyBjGFkMZWYREH04Bw4cwLp163S3Z4wJv7rSID/77LOGXR1GFEYkEsHo6KgpFaO8lqKiItW+ra2tcdsncklpV9Tt7OzEunXrVKTndrsNEQYZDzOKSbk0CJ3TDGH827/9m+5xQ6EQWlpaDBu/t99+G0NDQ2IRvsOHD6O/X/89X1ojQYRBBjcRzBJGJBLBE088Ib6Pj49PSBh/+MMfwDkXCmP79u04cuSI7rba+pPCMLs0yMjIiKF7TArDihgGnV+bQr1//37cdddduvtMZJy9Xq/hNjNRDIMGYUrCoPbMOcdvf/vbhNdhFSRhmAAZG6UPeXh4WBiFrVu34vnnnxfbKwOxSoPc19eHiooKQ+c0qjDMxknomGNjYyrCGBsbi5vU9cMf/jChS0pr3EdHR1WdOTc3V3dVzg8++ADvv/++qszn84Fzjn/6p38yfC16mUlmXFIdHR26xw2FQvD7/QnJSznprK+vD8FgEOFwGOFwGMFgEH6/H+FwWBWA37JlC/7nf/5HdZyxsTG4XC7DhME5NzxDeu/evXjmmWfEd7fbravk9uzZg+eee06o1fHxcezYsQPNzc2qZ6832KH6m10ahAYuRuJjZmIYZmONv/jFL3Do0CEVYTDGEAwGMTg4qLsPXePtt9+O9vZ2UX7XXXfpDrr0oOeSGhwcxLPPPgtATRjXXnstjhw5gr/+9a+48847xfZmMyTTgSQME1ASBmFkZEQ0qO7ubrz99tviNyKMQCCgIgzKiDECvQalNHAUw0hFYdB/JWH09PTEjdw2btwoXFJPP/103LH6+/tVBiIYDKpGjRMpjAMHDuDdd99VlQ0PD2N0dBSbNm0Sne7111+PIxYl9GR7IsLQbqvNptq8eTN2796NUCiE6upq9PT06J43Eolgw4YN4nt/fz/C4bBQGEQYmzdvxkMPPSS2a21tFYMMusZUFMZNN9004TbKNrJp0yaVcaXF9bT44Q9/CL/fj87OTtHu+vr6sHLlSkESwWAQ3/72t+P2VQa9kymMjz76CAcPHhT70XGTQemSmmyF0dvbi8HBwbjZ20Yyynbu3Kki71AoZOh6yAOh7d8bN24UqkPZXv1+P55//nk888wzWLVqldj+jjvuUB3TSkjCMAGaiEZptZxzBAIBQRjhcBi7du0S20ciERQUFMDn86mMOedctQzx5s2bJ3Rd6LmkbrrpJoRCIQwODmJ0dFQsU2JEYXz00UeqN+3RPAxqlF1dXXGLKnZ0dAiX1O9+97u4Y/p8PtUIMRgMqq63pKQE3/jGN+KuIxgMxqUbj46OYmhoCLW1tcJIvfjii6oRlRZ6CsPn8+kuDqndNhKJxLlntm7dij179iAUCqGkpGTCkWw4HFb91tfXp1rplO7D8PCwyui0t7eLc5577rn485//jPHxcTFCn6jTt7S0iDozxrBjxw7dQUIoFMK3vvUt8X3z5s1iobtE6O7uht/vR1dXl7gOxhjKyspE/b1er64i0wa9EymMTZs2iQEAGUsjC3lqXVKJnouWMLZt25Yw62l4eFi3/4yNjcHr9eLXv/61KHvmmWcQCATEcxocHMS2bdtU5zdDgFrC2Lt3L9xuNwA1YRQXF2PLli0YHh5GSUkJgOiz1noErHRLScIwgHA4jBdffFFXYWgJY/fu3eK3iQgDAMrLy8XI9tVXX8Vrr72me26tS4pzju7ubrz11lv4/e9/L0bzZWVlSRVGJBLB1772NWG4abVa5Yj2c5/7HAoLC1V+6/7+fni9XoRCId35JHqEMTIyoophLFmyJI4wQqFQnHQPBoPwer2oq6sT93VkZAQFBQUTXpcyLpGfn49gMDhhkF6rMCiDTXmPh4aGMDw8jFAohNLS0gkNUygUUhk6IgytwhgeHlaRkpIwVq1aheeff16lMCZat+qCCy4QA5acnByEw2GVoSIMDw+rjMjQ0JAwMFocOHBA+MN7enpUhEH3pLCwULStwcHBCQmDBh7ae7x+/XqVEhsZGRHqgIy4GQMbiUQSzvS+9tpr4wjjt7/9bcK44cjISBxh0PL/bW1tqjTpN954A0eOHBGGub6+XtWOjSoMQD+G0dfXh8rKSgDq9upwODAwMIDy8nKxbW9vb9xg1EqVIQnDAHp6evD888+rXtRDjUVLGErDQ4ShdUkBQEVFhVAVIyMjKleWEqQwDhw4gA0bNgh/+KFDh+D1esUotrKyMqnC8Pv92L17t6qO2pVYGxsbcdppp+HGG28U2w0PD2NwcBA+ny/OWJBhVBKGNoahvA4l9AgoGAxiaGgIdXV1aGtrQ1dXV1IXnpIwaORJxKEEKbuXXnpJEDvFl66++mqxnc/nw8jIiFAYE3X+YDAoCIPe+aEkDIorjYyMqAijr69PLDRYUFAgloEnhaGcF3PgwAHs2bMHkUgEbW1t2LhxozDKjY2N+PDDD+PqpSSMZCvt3nXXXcIYUh2VCoNzrnL/DAwMoKurK24US2nBVDfl7/v37xcuKCDaZ+h4NP+E7vH69etx4MAB3bpSBlMkEkmoMD766KM4wujp6RFtTc/nPzIyojvgGhsbQ2trqyqOEQwGVann2vurJIxgMIj77rtPt54TzcOgGCfnPM69Nzg4iEWLFglb1NPTYzplPx1IwkiAp59+GiMjI9i4cSNGR0dVhAFEU+J8Pp9ouNTpqQFEIhG4XC44HA7xUKkj0fu6aX+lIVa+K5gM0N69e0UsYWhoCK2trfB6vcI4ezweQ4Tx0UcfqbbTLhU9f/58NDU1ob+/H7t37xYj8KKiIvT09GBwcFDVgMlVoTSqWpeU8jqU0JtNrFQYzz33HF588UUAiX2z9FxycnIEQTudzjhDTy6NlpYWMSIMhUIYGRnBli1bVNc0MjKCYDCI0tLSuHOHQiHhoiPC+MMf/oCKigpDCoNcSqFQCE6nU+xDCqOsrExsf/XVV+PNN9/EyMgI5s6di/b2dnGtxxxzDPbs2YMHH3xQdb/9fj8OHz4Mzjna2towZ84c3fvGOcfo6ChqamqEC6+goEBXYZCBHxwcRHV1ddz6ZW+99ZYgDK2R6+/vV7lclQqD1oWiZ7Vt2zZVKvMLL7ygum9EZIkUxuHDh+NiQd3d3WhrawMA3H333XGEp6cwAAiFoYxzhUIhdHV1gTGGgYEBlJWVqfahwQIQJVi9uUV0fr0YBj0Haq90LznnmDNnDhYuXAiXy4VgMIienh6VypUxjCnEE088ge3bt+NnP/sZRkZGREegDu90OjE4OCj8w+FwGBUVFfD5fNi1a5cYQebm5ooO7ff7UVRUhLKyMgwMDOCGG27AwMAAioqKxLwIpUuCRuZ+vx99fX0YHR0VI32fzyfcP5WVlUlHGj6fD/X19aqJduSSUrqnnE4nBgYG8LOf/Qw7d+4UhrO3txecc0ESXV1d6OvrE75rgjLorSQ+PYXhdDpVZUrC2Lt3r7gml8sl3Ezr168HEO1AwWBQrCzKGBOEoTdTlwzO6OioMHhEiK2trcJgaF1S2hHkq2XaEmAAACAASURBVK++igceeEClMF588UVcfvnlKsKgYysJQznip7ZAz4EURllZmajf6OioUKhz585Fd3e3IAxSqY8++qjIqgGiCqO4uFi4K+gcSpSXl4tR8+zZs9HR0YHR0VFUV1djYGBApQiVxnlwcBBLliyJS7W98847xSKWWoUxMDCgSr3WuqSKiorEffT5fIJcOOe49957xX56Qe8DBw7EqdT+/n74/X6VKi0sLBSEoZcQoYxhKOtOz1JPYVAMUxsfUioMn8+n68ZNtgQJxReVhAFE3ZInnHACXC4XRkdH0d3drSIsmVY7RaDJZz6fD5s3b8bQ0BBcLpfqdZ0ulwsDAwNwu92IRCKCMDo7O7F27VrhkhobG8MzzzyDDRs2YGhoCKWlpWJ55oGBAQwODmLx4sXYv38/gsEgCgoKVIsPUqZGX1+fUBgUuCaFoR31cc7xla98RSXbfT4fmpqaVFk/SgMWDAbhcrngdDrR39+P1tZWHDp0CJWVlSgtLUVPT48qtvD444/j7bffhsfjEaNuOg4ZBGUcQ48wSkpKMDIygr///e+CMJUuKToOZSv19/fj5z//OQDgnXfewf333y8SCJQKQ2+k9cADD8DhcCAQCGDTpk149dVXEQqFEAqF4PV60dnZKe4TuaRKS0vjFtPz+Xx47733VNfsdrvxyU9+UkUYhYWFQmGQAdm1axeWLFkCIJ4waOJeeXm5qEtBQYEY/c6dOxc9PT2qyWXj4+Oor69XxQiGh4cxf/58dHZ2YmRkRDfgXVVVJUbJZWVlGBwcRDAYREVFhRid08BIG8NYtmwZtm/fLtKN9+zZg1mzZmHx4sXi3SFaN4pSYWhdUkVFRfD5fGhpaYHf7xfbhkIhcR8AtUuKZsU/99xzeOedd8Q2nHN4vV4MDQ2pCKOurk6QXCAQwPe//33VsSdajYHarHIQFwwGxYRMvXelKAljaGhIKBRl/6RrSbSWFGVZKu/lpz/9aVRWVooBVG9vL6qrqxEMBlXXYxUkYUwAMsTkMiLCULqkaCR+1lln4ZFHHkE4HIbH48G2bdvQ29urIozFixejt7cXPp8PxcXFcDqdYqTr8/nQ3NyM9evX45577oHL5RL1IANECiMQCCAQCKiChWQglIHOPXv24PnnnxejKiBqoGpra1UNl1xSlHPudDrhcrkEYXz00Ueora1FWVkZent70dTUJOT58PAwuru74fF4EAgE8OUvf1ncu5ycHJXRniiGUVdXh7fffhv333+/6GRerxdVVVXIyckRnbi6uhrd3d0IBAJ49913EYlEcPjwYfj9fhVhaNMtOef405/+BM45fvSjHwnjv2HDBtx7771CGVVVVQm3YCQSiSMM5bX4fD7s3LkzLujtcrlUhFFUVCQIg4z2tm3bsGzZMvE8iDCCwSAKCwsRDodRXl6Oa665Bm1tbaioqBAKo6mpCa+88gqefPJJ8czmzZuH8vJyhMNhtLa2CqVUU1ODkZERBAIB3YSByspK7NmzRwwG6JkWFRXFka02hrFq1SrccccdYs7RJZdcgrPOOgunnnqqbgyD2gTnHK2traJewFGX1NatW/HQQw8JhbF3717R5gHg3nvvVbmkaCC1bds2kQ5L56EBgJJUlYY3EAhgz5494pp7enrEswLUbh0a/VdWVqriZN3d3SJ2plWgyiwpmsj53HPP4fHHH8f27dtFnei9G3RcrdLQKgxlvUhhDAwMoLGxEW+99RY+//nPS5fUVIFGhT6fT4z4tYRBCuOrX/0q/v73vwvC2LJlC/r6+gRh5OXl4Qc/+IEgnpKSEuGDJMIoKyvDvn37sHv3brhcrri1pHw+H7xeLwKBAEpKSsTv5IOvq6tT+VkHBwdx/PHHqyYU+Xw+QRh5eXkAjrqkAAjCcDqd8Hq9aGtrQ2trK+rq6oRLas6cOXjhhRfAOcfIyAh6enpQUVGBjo4OHDp0CP/6r/+KQCCgIj1g4hhGXV0dXnzxRYyOjgqF4/V6UVhYCI/HIwiDOjQZwCeeeAJ/+9vfRDYWEV9BQQG8Xi/y8/PFjOXHHnsMra2taG9vF0akpaUF+/btQygUQnFxMZYsWYKOjg60tLQIFyIpIO0I0ufzobCwEKOjo3GEQe6xcDgs3g+hHOUfOHAA8+fPB3CUMBhjqhhGeXk5du/ejdbWVsyaNUsEZKn8yJEjwnhefvnlOP/88wFEg7kbNmzA8PAwqqqqhOtHT2E4nU4cOHAADQ0NKCsrQ09PDyorK+PcV9qg9+DgIFasWIHNmzejra0Nb775Jnbs2IGGhgYVcUciEQwPD4t5O5xzdHZ24vrrr49zSbndbhw8eFA83/7+fnznO9/Bs88+K1TM//3f/6mCwNQvaPR+0003IRAIwOv1ori4GIODg3A4HNi2bRv6+/tFEBmIEkZbWxv8fj/Wrl2Lq6++Gnl5eXExDIfDgdHRUZSWlqKpqUnMtaAlaCiLKpnCaG5uxvbt2/Hcc8/hz3/+MwD1EiQ0aPnKV76iOo6ewlC2NRpwNjQ0YOvWrRgYGLDk7ZxKTCvCYIydxxjbwxhrYYyttfJcRBhDQ0NYs2YN3G63rsIYHBwUIzgijK1bt6K8vFwQRn5+vnCVKAmDHrjf70dxcTE6OzvR39+vGhEqFQY11OrqasyePRtANOCrl3bq9/vR3NwsFMadd96pSxjUwRljGBkZES6p3NxcFBYWCoVBLqlTTjkFbW1tePDBBzEyMoK+vj6Ul5ejvb0dHR0deP311/HKK6/A6XSqRpm5ubno7OyMCx7W19fj1VdfRV5eHoLBIIqKigRhLF26VBgWIthAIIAzzjgD3/rWt/D73/8ew8PDwkiTS6q7uxvFxcXiOXq9XjzwwAM4++yzhZuis7MTy5cvx6FDh1BWVobm5mZ0dnbitttuE1lJE8UwfD4f5s2bh/b2dl3CIPckzfkYHh4WefXt7e2or68XKZJFRUUiKyo3N1cQxvj4uCCMQCAAv98Pt9uN6upqYawcDgfmzZuHc845B0A0u2ZoaAh+vx+VlZUqhaGN6eTn56O/vx8lJSUoKytDV1cXqqqqUFRUFJea6Xa7BXHTHJkVK1bgvffeEzPIGxoaVCNumon+0ksvieMcOHAAmzZtUmUZkkvq4MGD8Pl8yM/Px/DwMJxOJ5555hnRDzs6OlTuLqofudNokDY4OIiGhgZ4vV44HA78/Oc/F/2LEAgE0N7eDp/Ph0ceeQT79u3TTdSoqKhAT08PTjzxRCxYsADXX389tJiIMF599VVs3rwZPp8PCxYswN69e/GPf/xDKCulK9jj8aC3txd79uxRxTCJGLu6uuImD5L98Pv9qKqqwpYtW/Dxj3886Qq76WLaEAZjLBfArwF8CsASAJcxxpZYdT6lwrjllltEUJLy4DnnQmGQsR4bG4PH48HmzZuxYMECsSBbXl6eIAyfz4eSkhLhkhoaGhKzrTs6OtDf3w+XyxX3AiUajQYCAUEY1KCGh4fFiJ7IjNxcRBivvfaaiA0MDQ3FEYbH48GRI0eEwqipqcGiRYvQ2tqKE088ERdeeCF6enrw//7f/8O9996L9evXi2AmEUZXVxeWLl2KrVu3ori4WBUIz83NxSuvvIK//e1vACBcPnV1dXC5XIIQiouLMTQ0hIKCAqxdu1aoEqfTiXfffRf/+7//i1NPPRW1tbXCVUMKgwijq6tLzDvw+/3wer04fPgwTj75ZFXnPvnkk7Fr1y6Ul5ejubkZHR0daG9vFyPyRApjyZIl2L9/vyjjnMPpdMa9D5qMKLlQaGRZUlKCI0eOoKioSCwf43A4RAyDMYZDhw6htrZWpP4WFRXh61//uiAMbb36+vrg9XoxPDyM6upqESsoKCgQsRBCfn4+/H4/8vPzUVpaiq6uLsyaNQtVVVUqggPULimab/Hmm29i9+7d6Ovrw8UXX4zVq1eLayejvmXLFjFA4Jzj4MGDWLp0qSptdmxsDLNnz8Z7770Hv98v2n1paSk6OjpQX1+Pvr4+9PT0iBRcZWZfaWkpBgcH0draqksYIyMjGB0dVfUpUjFdXV1CVRYWFsa5TMvLy9HT04Pvfve7OPvss+HxeMRv5BKcyCW1fft27N69G0NDQ5g/fz727Nmjiu0p92tubsaBAwfQ0dGB7u5ulJaWAohO4MvLy8MLL7yAN998U3UOl8uF++67T2SM7d+/H8cee6zhVwmnimlDGABOAtDCOT/AOQ8BeBzAhVadTEkYZPyUhAFEjdjQ0JDI9KGgt9vtRmNjo1ALWoVRXFwsDGR+fj4KCwvjCEObVkuNPhAIoLGxEfPmzQNjTIz+nE4nFixYgKuuugpA1Kgdc8wxItDn9/vR2tqq65JijKG6uhrt7e0ihlFXV4fa2lr09/ejtrYWJ554Io4cOQKPxwPGGObPn4/29nZBGG1tbVi0aBGWLl2Kzs5O4XsnkJvH6/Wit7cX3/nOdwRhfOxjHwMAQRherxcFBQU4++yzxX1wuVw4fPgwPvroI1RUVOCNN97A6aefjnA4rDIihYWF6OzsRElJCRhj4py0PAkZ2YaGBpxwwgn48MMPUVZWhvnz5+PDDz/EqlWr8OlPfxoAVDEMwpEjR4R6a2lpEcthUB2VCkOJE088Ee+99564HiVhkOuKFEZNTQ2WL1+O1tZWYaTIiF900UUIBAITEgZleFVVVWHfvn3YsWMHCgsLRfsjKAmjrKwMnZ2dWLlyJb785S/D6/UKo0WuWNqX6u92u0W67LJly1BZWalySXHO8eGHH6pG9vv378eZZ56piquFw2EsX75c1Y/ovH6/H3PmzEFfXx96e3tVCxsqB1ShUAj9/f0TEga5OpV9CogmIKxYsQInnHCCrtuuoqIC3d3dyM3NxWmnnYaGhgbxm/K1w3Q8zjm6uroQiUTQ3d2N7u5uQRhtbW0qxax0SdGz6enpweHDh0XW0y9+8Qs0NjbC7/ejo6NDRQYFBQV49NFH0dPTA7fbLQZ9E036nCxMJ8JoANCm+N4eKxNgjF3DGNvEGNs00fo/RkFrGhFh5OXlqVxSlFY7OjoqGi8RxvHHHw+Px4Oenh64XC5dlxTFCTweDwoKClQuKaX/XxssDgQC+NKXvoQzzzwTt912Gy6++GJBPDfffDOKi4sxMDAAv98vMl7uu+8+BAIB7N+/XxBGfn6+8J3n5OSguroabW1twiVVW1uL8vJynHzyySgtLYXT6cTw8DBqa2sBREd2R44cQV9fH8rKykSHWLZsmXB9KWU0KSGv14sDBw6IZcCXLl0q1kQaHR1VEYYSlGDQ09ODgoIClJWV4amnnhIdT+mS0iqMvr4+5OXloaioCLm5ucjLy8OiRYswd+5c7N69GxdddBFOPfVU7Nu3D83NzbjxxhvFumBKl1QoFMLNN98s8uFbWlpEwLiwsFCMYhljKpcUAJxzzjl46qmnhCEuLS3F4cOHVYRBxq+hoQH/+Z//iUOHDgnCIIWZn58vFpzUTmYkNUUuqTfeeAMvv/wyCgoK4ia6kc9eqTCo/rm5uSgtLRV1n2jUWlZWJtKqgaNqiow6zUcoKSlBZWUltm3bhpNOOknVnkm5f+5zn4tLsc7JyUFTU5PIliPC0M4dOnLkCJYsWYK+vj4cPnwYixYtEoRB/VjZp4qKilBVVYWdO3fi0ksvxec//3ldwigvL0dvb2/cfVaemwiDBoAXXXQRXC4Xent70d3dDZ/Ph/nz56O4uBh33nmnGEgoXVIFBQXo7+8XEy5pkFJYWIgbbrhBxGqU1+ByucSsfLfbjdWrV0vC0EAv/K9KxeCc38c5X8k5X1lVVZXWycjVoQwEE2EQqJEA0VELjf4vvPBCVFRUoLe3V6UwKOuKYhhdXV1obGxEYWEhCgsLMTQ0FBfDUOZpK+d9MMZQW1uLM888U7WcxMqVK7Fjxw5BdOPj43jooYcQCASwd+9eMfrSuqSqqqrQ1tYmrrWhoQHLli3DI488gnPOOQf5+fmoqqoS+5WVleHIkSPo7e0VLqkrr7xSpJYuWrTo6IOLGSEyaAcPHhTzNFwulwgCUwxjaGhIdGCS7i6XC4ODg4IwgOjaOsrMGT3CGB4eRm9vr1B+ubm5cDqduOSSS0RcprGxEcXFxXC73TjmmGMAAIcOHcILL7yA4uJiYTAotRmIZm1R5+7t7UVRUZFQGLRshxJz587Fm2++ieOOOw5AVGG0tbWhpKRExDCIMHJyclBUVKQiDFIYFATVUxhEJqQwOjo6cPjw4aQKQ6uUi4qKUFpaKmad03PQGmqac0SGTKswgGjGW21tLRobG4Xbq7CwUKS/jo6OIi8vD/fff39cH6yoqMCcOXPEvKFgMKibnt3R0YF//ud/Rl9fHw4dOoTm5mahKpUuKbqOkpISzJo1Czt27MAnPvEJfOlLXxKpzkq4XC4MDw+L+0wDAGWMRzlhtLW1Fe+8844gzp6eHqEwysvLcfrpp4tjK11SBQUFaGtrg8fjQXt7O0pLS0W/p20OHToURxikqGfPno1PfepT8Hg80iWlQDuAWYrvjQCSL9SfIshnTCNG8rMr5bDT6cTixYsBHB2NVFdX40tf+pLwSydySXV3d6OhoQEFBQXIycmB2+0Wri+9Fyi53W7ViI5+p9EpEDVMFECkzKKBgQHh1qqqqhIxDOVojRQGEWNlZSVuvPFGMMaQk5OD/Px81NfXi/OWlpYiGAyiv79fkMfpp5+OmpoalJWV4ZhjjlGNzJQuqYMHD+ouNqcMetM10sQoIktSbQQynIliGFVVVSIDyOFwwOl04mtf+xpcLpcItAJAbW2tIDpKH3Y6ncKfTymfPPY62O7ubpSUlAjCoBgGKQwtVq9erSKMffv2oaamRrQVckmRq1HpkqKgN9WV3gqnB6/Xi+rqanR0dAiCTRTDAKAyqm63G2VlZaqyf/zjH/jCF76gSmSoq6sTL8cCjioMZapoWVkZamtrMWvWLMyfPx/19fVi+1tuuQXf+MY34HA4dNNBlYRRXl4u2rly8U8g6u675JJL0NfXh87OTsybN0/Mw9AqDOoDjY2N2L9/P2bPno2SkhLdd6LQStPUjinQrAQpjMLCQtx///04/fTTVYHrQCAAj8ej6juA2iXlcrlw6NAhzJ07V7iktITR3t4eRxiLFi1CaWkpTjnlFBFjkQrjKN4DsJAxNpcxlg9gDYDnk+yTMogwlPEKrcI49dRT8cgjjwCIEkZXV5cYgZOhJ8IglwARhtPpRFdXFxoaGsRouri4WEzK0abVUr66ljBycnKEsQGihHHgwAGRGVJfXy/Wmlq9erUgmPz8fJUrR0kYSiNJcDqdcYRRUVGBSCSC8vJykVkFRDv64sWLVY2X3BRer1dlCJWgGEZfX5/wfVPQlhTG8PCw6vqVLhAacXV1daG4uFhkKNXX18Pj8agUBgBh2OmZ/fjHPxbvKXnqqaeEO5HuhXJSmcvlwsjIiEphkBIiV58Wd9xxB1auXCnuX2dnJ4qLi+NcUqQwiEABiAmfZOBp0pcSypn1hYWFIiVVqTBuv/12APGEQfcYOKowaIkKIJqySxlUyuuhxA5lHUhhMMZQXl6O2tpaLFy4EB//+MfhdrtFO6KVYOk6QqGQeBbUjubMmYNt27Zh9uzZujEMIDohs6amBn19feCcC1LKy8uLUxiBQECo5/z8fFRXV8c9J+WyHYFAQLTj4uJi4WalvqMkjI0bN+KMM87A2NiYcD0C0Xa2Zs0a1Tm0LqnDhw9j9uzZKoWhVHQUhyG4XC6ceOKJqvPQ0jRWYtoQBud8DMDXAbwC4EMAT3LOd1pxrscee0zkvZPLiRSG8iGSbxaAyGWnxk9uCWq4tGYMLdLmcDjQ29srFAYQ7ajV1dVxE/d27dqFuXPnCiOgJQylS6q+vh5HjhwR+feLFy9GXV0djjnmGHz3u98FcDTTRelCqKysFKMYp9MZ59N1Op044YQTxPfS0lLVtSs7XmVlJWbNmqVaVZPuC01kUhoGQm9vL2pra4V7BogSxgknnCAUBgDV9Q8MDKCkpETlGuju7sbs2bORn5+Pvr4+1NfXxykM5XWR0aTRP933yspK5OfnC6OtVBh0DyiWQ6SyYsUKQRjaa3S73aq2Q9lQNGuZFAapTUowoHNT/el+ao0DY9E3QC5evFg8P4fDIRRGIBDAli1bxExpJWHMmTMnziWlNLTl5eUoLy9XzVVobm4W/UIJMuqcc1RUVKC2thbNzc245pprAESDuaRG8vPzxX0KBoNYvHix+I0IY+vWrZg/f34cYRA5UZ2HhoYQiUREAgclhCgnpA4MDKC+vh633normpqaVMRDSo+Qn5+vUhhKwqDPyna3dOlSESdTzpUCoivoKqENend3d6O+vj5OYdAChTQYIZSVlWH16tUqIiooKBADAqswbQgDADjnL3LOF3HO53POLbszGzZswMDAACoqKgRh0MhbK4cJbrdbFRvQKgzFNQCIdm6v14v6+np85zvfARDNx6ZlQ2gbh8OBN954A2eeeSbcbjd6e3tVDUerMGh0RyO8a6+9FkuXLoXL5RJ+Wpr5qnRJEaE5nU40NzeLyWAEh8OBH//4x+J7WVkZPB6PUE9KwqitrUVOTo5qHSCqH3U+7f1jjGH//v1obm5WEc28efNw/vnnC4UBqAkjGAxi4cKFqo570kknobq6GvPnz8fWrVsxb9481NXVxSkMAGKErwcijO9///sAIAwE3f+ysjIsW7YMW7ZswQUXXAAAuOGGG8S8EKWS0aKkpAQ1NTUAIIwmLROhJAwCGUciGD3CoGN94QtfEG7OOXPmqBQuTSrMy8sT2XVAlDDoupYsWYL6+vq4CZh1dXVxyQhERsp6KpcG+drXvibctoS5c+eKz2Qc6Vkcd9xx4jl98YtfxPz58zE+Po758+cjGAyqRt6UZkv3WJkOTIRJqcUulwtutxu//vWvxb3Rxi3olQP0vPQUBk2MI8JQKoxTTjlFzNfyeDxYvnw5JoqlamMY3d3dqKurEwpD6YouKirCv/zLv6iexTHHHINvfOMb+NSnPqU6LrUpqzCtCMMueDwetLS0oLa2Vjw0vRiGEvTaS6Xh1iMMJTjnwt8PRDskZdvQ77m5uRgcHERZWRncbjcOHz6sMqjaGAYQb6C0/m7K+lL6/oHo8hhOpxMlJSVYuHBhwntEo2vK+FISBqX2KkfYSsLQM3Sccxw6dAjz589Xvb72nnvuEfGBcDisIlQget8XLlwoCNLlcuGxxx4DACxatAgffPABbrjhBpxwwgnCbaQlDD21A0QJw+l0iriG3+/HrFmzxPfS0lKcf/75uP/++1WGgSZBkjLRWxBOSxjk0iPCKCoq0nXb0fYOh0N3yfenn35aEHZ1dTVmzZolZsBTokFeXl6cwpg9e7Zod5dddhnq6urg9XpVba22tlaVJgscdXcR6PjUb2iSohZ0T5SEccwxx+D4449HUVERCgoKsHz5cuTn52PWrFliPopykKN0J9J9Of7441XnobXJXC4XCgsL8ctf/lLUV/syMMoqVB5vIoVRUlKiIoyLL74YF110kUhMqKqqwkUXXYQLL9TP/Ne6pIgwOjs7UV1drUp2KSoqwnXXXYcrr7xSdQyrlwHRgyQMHXg8Huzbtw91dXUieOp0OsWSwxQIV0K5QBtw1FVUXl6OU089VZRr14NR+iCvuOIKFWEAUQM7NDQEt9stXFJKd5HWJUVQGiltp/7tb3+LE088URXDABDnDkuE0tJSsXyH1hes9z5uSmclN5z2HtI7EQoKCuImSNG9AqKkpqxjYWEhFixYIEZsjDGxnPdxxx2HHTt2CCNXX1+P008/PS7FMpnCIPh8PsyZMwfNzc0AosZOb18a7VZVVYkMMb37R8RDBpwUBqkISmEmFw2B4k/K49L1K9tGXV2duBfkkqJ03Pz8fFXA/7Of/SxWr16tew8ItbW1cUuHaNurljCSobS0VBj9m266CZWVlXC73ar2+rGPfUyVGEB9UOm6BIDly5eLGBFBSxiLFy8Wx9YuS15eXo7TTjsNs2ZFc2v0FAYtOVJeXo7h4WFx38vKyoSKyc/PR0NDA+rr63XvKRAf9O7t7UVdXR1WrFgh2oKSMEg9TzUkYeigsrJSuH6oURlRGNqZzRSo/sxnPgNAHZQEoiSkJIyLLrpIFXwkl5Tf70dhYWFcIBrQJwxalkRZN2VjW7p0KZYuXRqnMKqrq+Ny4SeCy+XCN7/5TXg8HlRVVcW5sLRwOBxwuVx44IEHcNVVV8WNumnZk/z8fN2RNRm2yspK1bV897vfhcfj0Z1xO2fOHKxdu1aQflFREc444wzV/olcUrfccovqvvr9flx33XViAEDzU7SgOTpEbnrPzel0itfOksJQzrlhjOGuu+4CEHUzKOcV6bmktHMNgChh0Nv0iDBoSRGKm9C119TUqNoicDSOQ2hqaop7t0YihZFoBKxc2kOrQEhhEB588EExoFAuxqd9sdY3v/lNsbAjYXx8HF6vVxDGD37wg7gANOH000/H5ZdfjhUrVogBzujoqDhHTU0NWltb4XQ6hXLTpjcTYdxzzz0TXjvVS6kwIpEI5s+fL1QEvWCL7kemYOLXmM1geDwe4QYhSU4xDG0uOoGkKEG5vtFE0CoMQN0BlY3R7XbD7XarOjCdRxnDACAWlFPWTZu7TiNf5fWYIQwg+npRj8eD4uJinHvuuQm3pRHSRD5d8vvm5eWpXFLK+tKEQuV9JgOhDIAq8R//8R+65yIkckkde+yxqu80e14Zw9AjGwpQEmFMpNrovufn56uy1giUTTRnzhzVu+L1XFJ6hHHaaaeJMq3CoGueiCyBqNHSEsZXv/pV1TZawqD9xsbGkr6bgVyyWsLQvhyJ6qtVGMoYBhDvoqHAOymMc845R7h29TBv3jwA0b6+fv164ZKivnX88cfjnnvuQV1dHWbNmiUUrZYwKMlFD9r5G8DRmNzs2bOFCoKZDAAAG01JREFUK5hIHZCEkfHweDyora0VE7GAaK53bW1tQoWh9dnrEYZ2UTdt46VsKAJ1CJqtqyUMvRhGc3OzqrO63e64eQE0wUxZxxUrVsTJ9GRQTtBLBuW1KnP1AQgDW1BQoJvqCET9xpdeeqnu/Z/ouehBaeASuaS0oEwlwmc/+9k4nz5wdDkNuh69WcRKKBWGnitnzpw5queipzBooUslbr75ZvGZgt7KGAYdayLk5uZOSPAEvaB3qi4pgnKpcYKSMPSC3nqgQRIpDOXSHolQU1ODO+64Az09PaoYRlFREfr7+9HU1ITGxkZBGMo+nqw9ORwOeL1elUsqNzdXDFCV29F9kYSR4fB4PKipqVGll55//vno6OiY0DAVFhYaUhjKTl5dXR13LMrIAY6qAOXEPj2FoXVJffKTn1Rto3wFpnI/LalRsNoMyK1iBNoF7eglNEDU8CxYsACzZ8/G2rX6CxGXl5fj8ssv1/1NzyU1EbQKwyhhKN9fAWBCVWXEJaUEGfCJcuiNEEZ+fn7C+BMFvc0oDD0FrAXFsgicc9U7yZOhuro6jlC1LlTgaIxLG/RORBg0IPF6vaaUMxDtp7REvvI+X3DBBSJFl5a/0XNJTYQbb7wRP/rRj/CFL3xB1V61xCwVxjRCXV0dLr30UgwNDakeFs1bMOqS0r5XuLa2VmW4tS4PLcjfTB3KKGHo1U1P6eity2MWZvZV3suCggLU1dWJ7ytXrlQFgfWQSP1M5JLSg9JAXXXVVRMqGi0mmj+iBWUleTwe4TtPBKXC0LuGBQsWiNRr2l7rkqIVkScCuaRoTTQKmie6npqamqTP97bbblPVgxSGUcK47rrr4q55yZIlqrYBwJBLSgvKiCOFYRZ0b5TnUM6nuOmmm3DdddfpuqQmAs1CVyoMIDFhUJJFJkAShg6cTifOP/98lf8SOPqeXb1OXVhYGLcUhlZhNDU1ibe6AdGZssngcDjECLWmpgZf/OIXVb/ruaS0mChYrg16Ww2twlixYoX4fsYZZyTdPxFhmHFJ0SteAYiUZiMwqmJycnJw8803CyOeTGEoYxh6x8/JyREZU7R9KgpjdHQUdXV1QsUWFhYmHGgYbZ9KEGHQGlHJoHe9c+bMiQuuK5eAp0EOvWAo0bEpezFR/5gIeoShxOmnny5cu4TKykpcdtllEx5TL4YBIG7QoiQMvTjcVEESRgJoR2zUSPUME62GStBTGHPnzlX5ZpMZOFIBNELNz8/HiSeeqNpGL+itxdlnnx0X9Cb/qxlXTrpQjrTPO+88seigUSQjDKPXkczNkugcRkiJMYZVq1YBgCGXVDKFobe9ljCSKQxlzEjpk08EekmXGRBh+P3+hG4gGnwZBS2bopwUONFcFAIRhjJ70QyUk3AngjbonZeXF5faqwftQO3kk09W/a4kjEyCJAwTSEQYgNqgkcJQbnvqqadi+fLlhs9HLqlEBseIS0pv5EnzIOwkDOV1GOlUWiTKxLLjOrRvopsIykDoypUrkxpm5TwMo4ShXUsqmcLQgxW+cSIM5SxyPZAyNgo9l5TeelpKUNxPG78zClJ8ic4x0TybZNC6pP793/9d9bskjCxAMsJQjlz1gt55eXmqmbNGkJubmzZhTLQf5ZFbPWOUMrbS9cV++ctfTngOO67DKGHQ80gWpwLUM72NEAbN0teObDOBMACIuUOJ6kPuUKOgV/gqXVJGFYbR7Cg9JEpGANSDAzNINsDJVMKQE/dMgGQ9jaK0SEYYZkEqQC91k0BKxqyPlhq6nQrDSl+s1ddBa3QZ3dYMgc+bNw9f/OIXDSsMZdCaQEvom4FVCoOWHZlMwtC6pIwShtvtximnnGLqGpSgNb4mQqoKI1nsUBJGFoD8rowxNDY2xv3+k5/8RHyeaB6GGShf5zkR6PipKgw7CcNKWH0d2uU5km1rpi4OhwP19fWGFQbND9JmSSVTGFqFNNUuKbMKIxXCWL58OT7/+c+bugbtec3EMIxC65LSIlMJQ7qkTICMRm5ublwWBwAViegFvVPFFVdcMeFvk0EYU7GI2WQjkxRGqkbEjMLQnsOIwtA+52TB+FSQk5NjKOidisKgeRjA0WVzkmVJ1dfXx62WawbKybsTnSOVdicVxgxAMsJQYjJcUoTzzjtvwt+Us0XNgNxdZuYvZDKsJj69Je0nQm5ubkp1MbrftddeG+f2SiWGkUi5pgpaKt+IwjCq2IDo9dG7T+g8RoLeqaTTKmGEhGeSwpj+lsJGmCGMdF1SpACMbmf2PNnmkrKa+My4pFI1UkYVxurVq+OM4Zo1a7BgwQJT57v77rtN1zEZSGFY4ZIKBAJxL+VKRhipGHMlrCSMRM+aZppnGqRLygTILXHVVVfFzUTV25aWqk7nXEa2S6XBkpQuKSnJCsKwwyWVKYRB9VE+92QDGD1Y4ZJSrrA82UHvyspKcX+MxjDsUBjSJSWhCzL+q1atStpI7FQYqRAGuaTq6uqyIoaRqhvIKMy4pIzMcNaD0aA31Sfd0bMVyMnJEVlSk60wUiGMdO9RMjdfqs+BXqE8ESZ6ncFUQyoMEyCDZNSQp0MY2hcMTQRteqVRECHV1dVlhcJIFpxMF2aC3qkaKbMKI93RsxUwE8NIhTCUfXA6xzAGBwfF8vV6WLVqVUqTW61G5rW4DAY1VqPrCaWrMIxul0qnIMKor6+XhGEAdsQwzCiMVEfPRkkvVVgVw3A4HKiqqrJdYSQjjFSzpOi1yxPB6IDRbkx/S2EjzCgM6ZI6CquNFGCPwsjkGEamwAxhmM2S0nNJJUurzdSgt9frNf3umUyAJAwTUDZWI9umE4g1OsJIN+i9ZMkSSxuuGVdOOnA6nRkTw0iVMMw8SzKYmQYyoGNjYwn9/2bIEYi+C+Xqq69WPWM7gt7J4gjpuKRSXQRzKpF5LS6DYVZhAMZdS1oYNbSpxjCIkC6++OJUqmfqPFJhGIfRzJhMVhhEZskUhpn6OxwOrFixQrwewGgMI9179N///d8Jf081SyoQCKT0jo6pRlo9jDF2KWNsJ2Mswhhbqfnte4yxFsbYHsbYuYryFYyx7bHf7mYx68sYczLGnoiVv8sYa1LscyVjbF/s78p06pwOzAa9lf/NwoxLKp0YhtUw4/tPB3bEMKxWGMD0JwxqU3l5eZNKGNrj25VWa5XCoH2nG9LtYTsAXAzgLWUhY2wJgDUAjgVwHoDfMMbort4D4BoAC2N/NI35KgADnPMFAO4CsC52rAoAtwL4GICTANzKGDO35OskIRXCSGcehtUuKTsarF0uKVouwyqYuY50jJTRlNxMzZKySmEojw/YF/ROhlQVxnRFWlfKOf+Qc75H56cLATzOOQ9yzg8CaAFwEmOsDkAJ53wDj/a+hwF8RrHPQ7HPfwRwVkx9nAvgNc55P+d8AMBrOEoytsJOl5TRhpiuS8pqZJPCsMMlZZQwMnkeRiYRhtWkmqlKzypY1cMaALQpvrfHyhpin7Xlqn0452MAvAA8CY5lOzLVJZVO0Ntq2DX6cjqdWRHDMEoYn/vc59J6z4NVoIGIw+FIOtM7HZcUEA2EJ3tXjB0KIxPTm61C0pbNGFsPoFbnp+9zzv800W46ZTxBear7qE/K2DWIurtSer2kEZiZUAfIGEa2BL3NXEc6RspoDGPhwoUpn8NKmFEYqTwvZbu97rrrErZhOxTGTHNJJb2bnPOzUzhuO4BZiu+NAI7Eyht1ypX7tDPGHABKAfTHyj+h2eeNCep6H4D7AGDlypWWWCkz6a7K/6mcx+gEwUx3SdlxnpkWw8hU0POmd5RPhHRdUnSuZNtmqsKwIwXcinNYRY3PA1gTy3yai2hweyPnvAOAjzG2OhafuALAnxT7UAbUJQD+EotzvALgHMZYeSzYfU6sbEpgF2EYVQAsthSDVcdPF3adJ5MURjpG3+oF56x+FvQM3G53wnPZEXuzS2FkWgzDSlWf1t1kjF0E4H8AVAH4M2NsC+f8XM75TsbYkwB2ARgDcD3nnNYBuA7AgwAKALwU+wOA3wH4PWOsBVFlsQYAOOf9jLHbALwX2+6/OOf96dQ7HZh1SVmdJQWkZqCyUWFkSgwjHQMy3RUGtdtk6ajproFmdFurjbldsUAzoAQNK+qVFmFwzp8F8OwEv90O4Had8k0AjtMpHwVw6QTHegDAA+nUdbJgJraQzgMzs38qo1I7g97Z4JKyK0sqE5e0NgMaICR7/atUGNbBylT2zKLGaQAz6a7pGDAzHSNVwsgmhWFHlpTRTvjtb3875fNYrTDsWHxwJimMTMySyliX1EyEmRhGugpDuqSMY9WqVZaOJs10wubm5pTPc8cdd6S8byaAnncmEMZxxx2XcAnxyUAqWVLj4+OWtlUz7lOzkIRhEnYShtUuqWwKes+bN8/S41vZCZWoqKiw/BxWgp73mWeemXC7yZiHkQynnXaa6eObRSoKIxwOJ10FNx1YqTCkS8ok7CIM6ZLKLNg1Y326g9r81VdfnXA7OxSGHUhFYYTDYUtdj1YObiRhmISZdNd0ZKcdLqlsCnpbDbvWxJruMJNFmMnzh4zC4XCkVB8rCUMqjAyCmQabLmEYNeiZPA8j0zp4qpAKwxiMtqtsURip9L3c3FxJGDMFZgxgOo3CTMeQLinrIRWGMdihMDJp3kMq12A1YUiXVAbBLoVh5jw//elPLT1+Osi0EWGqsHLUlk2wWmHQOTIFqa7jZuV8G6kwMgiZ6JKqqqpK6fh2KYxsgF1ZUtMdkjCSQyqMGQQzhtauoHcqkEFvc8gG11omuSDTIYxMmlmdiS4pqTAyCJnokkoFMoZhDpm4ZpBZ2OFSM3qPHA5HViiMH/zgB6b3kYQxg2Bn0NvqpS4kYRhHpgVbMxUzTWE0NTWZ3ke6pGYQzBiOTHdJyaC3cWQLYVj9LOyIYWQSYaQCqTBmEKTCMIdsURjZQnx2LT6YDNkS9E4FUmHMIGRTDEMGvY0jGxSGHZleRtvtokWL8NWvfjWlc2SDwpBptTME2ZQlJRWGcWRD0NuOyYdm3hdTUlKS8jmmM6RLagYhE+dhpHp8SRjGIRWGMdgVF5vOkC6pGYRscUmVlpbipJNOsuz4hGxxSWWDwrBjPSw7nnc2uKSkwpghyMS1pFJBUVERLrjgAsuOT8gmhTHdr8MOY27XOaYzpMKYQcgWl5RdkAojc2DHs7CDWKXCSAypMDII2RL0tgvZMDIHsieGYfU1SIWRHFZnSVmZ3DC97/wUwOoXGxGyydBmw3Vkg8Kw41nYcY7p/hzsUBjSJZUhMNNYpUsqu5TSdH8edikMqzHdXVKlpaUoLi627PhWuqRSt2gzFHYSRrYY2my4jmx4HjLonRm4+uqrLZ+Um5EKgzF2J2NsN2NsG2PsWcZYmeK37zHGWhhjexhj5yrKVzDGtsd+u5vF7hxjzMkYeyJW/i5jrEmxz5WMsX2xvyvTqXO6sIswpKHNLEiFYQwy6J0cdrgFMzWG8RqA4zjnSwHsBfA9AGCMLQGwBsCxAM4D8BvGGD3lewBcA2Bh7O+8WPlVAAY45wsA3AVgXexYFQBuBfAxACcBuJUxVp5mvVOGmca6du3alM+TLS6pbCK+6f487IovSMKYWmRs0Jtz/irnfCz29R0AjbHPFwJ4nHMe5JwfBNAC4CTGWB2AEs75Bh69oocBfEaxz0Oxz38EcFZMfZwL4DXOeT/nfABRkiKSsR1mGuvChQtTPk82jcyz5TqmO2Fki8KY7s/BakyXoPeXAbwU+9wAoE3xW3usrCH2WVuu2idGQl4AngTHigNj7BrG2CbG2Kaenp60LmYi2NVYs4Uwsuk6pruhkjGMmYEpDXozxtYDqNX56fuc8z/Ftvk+gDEAj9JuOtvzBOWp7qMu5Pw+APcBwMqVKy25Y3Y11mwY0QJSYWQSssUlNd2fg9WwMuidlDA452cn+j0WhL4AwFn8KK21A5il2KwRwJFYeaNOuXKfdsaYA0ApgP5Y+Sc0+7yRrN5WQSoMc5DXkTmwyyVlNWQMIzEyNujNGDsPwM0APs05H1H89DyANbHMp7mIBrc3cs47APgYY6tj8YkrAPxJsQ9lQF0C4C8xAnoFwDmMsfJYsPucWNmUQBKGOUiFkTnIFpeUJIzEsDLone48jF8BcAJ4LdZI3uGcX8s538kYexLALkRdVddzzsdj+1wH4EEABYjGPCju8TsAv2eMtSCqLNYAAOe8nzF2G4D3Ytv9F+e8P816pwy7Gms2GCgge4gvW2IYMuid/bAy6J0WYcRSYCf67XYAt+uUbwJwnE75KIBLJzjWAwAeSL2mkwe7Gmt5+ZRlDk8qpMLIHGRLDEMqjMSQM70zCHYZjVWrVtlyHquRLYQhFYbxc0iFMbXI2JneMxGysZpDtriksoH4smV5c9kHEyNjg94zEbKxmkM2GFpAKgwz57Aa0iWVGFJhZBBkYzWHbFIY050w7CBvqTCmHlJhZBBkYzWHbCALQCoMM+eQQe+pRcauJTUTIRurOWSLSyobFEa2xDBkH0yM6bKW1IzAdDcadiNbXFLZcB3ZojBkH0wM6ZLKIMjGag5SYWQOsmUexnR/DlZDBr0zCFIOm0M2jMyB7CAMu1xSVmO6PwerIRVGBkE2VnPIFoUhg97GzyFjGFMLGfTOIEx3o2E3soUwskFh2PEsTjnlFJSWllp6juOPP97S4093ZOxaUjMR091o2I1scUllw3XYoTA+9alPWXp8APjmN79p+TmmM6RLKoMgCcMcpMLIHGQD6Ukkhwx6ZxCk/9QcssVIyRiGxHSBVBgZBNnhzEEqjMxBtjwLicSQQe8MwnQ3GnYjW4xUNozOs+EaJJJDzvTOIMgOZw7Z4pLKBoWRLc9CIjGkSyqDIGMY5pBNCmO6X4dUGDMDMuidQSgoKJjqKkwrZIOhBbJDYWQLeUskhlQYGYRf/OIXU12FaYVsMVLZMDrPhmuQSA4Z9M4g5OXlTXUVphWyhTCyQWFki9qTSAwZ9JaYtsgWI5UNo/NsuAaJ5JAKQ2LaIlsURnl5ORYuXDjV1UgL2fIsJBJDriUlMW2RLQqjpqYGF1544VRXIy1ky7OQSIxjjz0WoVDIkmOnpTAYY7cxxrYxxrYwxl5ljNUrfvseY6yFMbaHMXauonwFY2x77Le7WawFM8acjLEnYuXvMsaaFPtcyRjbF/u7Mp06S9gLOarNHEiX1MxAQ0MD5s6da8mx0209d3LOl3LOlwH4PwD/AQCMsSUA1gA4FsB5AH7DGKMJDPcAuAbAwtjfebHyqwAMcM4XALgLwLrYsSoA3ArgYwBOAnArY6w8zXpL2ARJGJkDqTAk0kVahME5H1J8dQOgSMuFAB7nnAc55wcBtAA4iTFWB6CEc76BR6MyDwP4jGKfh2Kf/wjgrJj6OBfAa5zzfs75AIDXcJRkJDIcLpcLTqdzqqshgezI9JKYWqQdw2CM3Q7gCgBeAGfEihsAvKPYrD1WFo591pbTPm0AwDkfY4x5AXiU5Tr7aOtyDaLqBbNnz075miQmD8uWLcPSpUunuhoSkApDIn0kHW4wxtYzxnbo/F0IAJzz73POZwF4FMDXaTedQ/EE5anuoy7k/D7O+UrO+cqqqqpElyVhI+SoNjMgYxgS6SKpwuCcn23wWI8B+DOi8YZ2ALMUvzUCOBIrb9Qph2KfdsaYA0ApgP5Y+Sc0+7xhsE4SEhIxyHiSxP9v7/5CrCjjMI5/H9zVJIU0M0QlN5BAJEoihI2QCDMLDSrYi8iLQIouii5CEYIKobqIriMl6Z8EJYoQJVl0l39yrTU11zISl7aQyC6qTX9dzLs0u5yVcY82c2aeDwznnd+Zs7zPwu7vvDOzZ9vV7l1S+RvT1wDH0ngX0JfufOohu7i9LyKGgHOSlqfrE48CO3OvGb0D6iFgb7rO8TGwUtKsdLF7ZaqZ2SXwCsPa1e41jJck3QRcAH4EHgeIiCOS3ge+Bf4BnoyI8+k1TwBvAtOBj9IGsAV4S9Ig2cqiL32ts5JeBPan416IiLNtztuscXwNw9rVVsOIiAcv8txmYHOL+gFgaYv6n8DDE3ytrcDWyc/UzLzCsHb5L73NGqK3t5eRkZGyp2EdzA3DrCG6urro6vKPvE2e16dmZlaIG4aZmRXihmFmZoW4YZiZWSFuGGZmVogbhpmZFeKGYWZmhbhhmJlZIco+369+JP1C9vlWkzUH+PUyTaeqmpARmpGzCRmhGTnLznhDRLT8/xC1bRjtknQgIm4rex5XUhMyQjNyNiEjNCNnlTP6lJSZmRXihmFmZoW4YUzs9bIn8D9oQkZoRs4mZIRm5KxsRl/DMDOzQrzCMDOzQtwwzMysEDeMcSStknRc0qCkDWXPpx2StkoaljSQq82WtEfSifQ4K/fcxpT7uKR7ypn1pZG0UNJnko5KOiLpqVSvTU5JV0naJ+lwyvh8qtcmY56kKZIOSdqd9muXU9IpSd9I6pd0INWqnzMivKUNmAKcBG4EpgKHgSVlz6uNPHcCy4CBXO0VYEMabwBeTuMlKe80oCd9H6aUnaFAxnnAsjSeCXyXstQmJyBgRhp3A18Cy+uUcVzeZ4B3gd1pv3Y5gVPAnHG1yuf0CmOs24HBiPg+Iv4GtgNrS57TpEXEF8DZceW1wLY03gY8kKtvj4i/IuIHYJDs+1FpETEUEV+l8TngKDCfGuWMzB9ptzttQY0yjpK0ALgPeCNXrl3OCVQ+pxvGWPOBn3L7p1OtTq6PiCHIftkCc1O947NLWgTcSvYOvFY502mafmAY2BMRtcuYvAY8C1zI1eqYM4BPJB2UtD7VKp/T/xF+LLWoNeW+447OLmkG8AHwdET8LrWKkx3aolb5nBFxHrhF0jXADklLL3J4R2aUdD8wHBEHJa0o8pIWtcrnTHoj4oykucAeSccucmxlcnqFMdZpYGFufwFwpqS5XCk/S5oHkB6HU71js0vqJmsW70TEh6lcu5wAEfEb8Dmwivpl7AXWSDpFdjr4LklvU7+cRMSZ9DgM7CA7xVT5nG4YY+0HFkvqkTQV6AN2lTyny20XsC6N1wE7c/U+SdMk9QCLgX0lzO+SKFtKbAGORsSruadqk1PSdWllgaTpwN3AMWqUESAiNkbEgohYRPaztzciHqFmOSVdLWnm6BhYCQzQCTnLvlugahuwmuxOm5PAprLn02aW94AhYITsXcpjwLXAp8CJ9Dg7d/ymlPs4cG/Z8y+Y8Q6y5fnXQH/aVtcpJ3AzcChlHACeS/XaZGyReQX/3SVVq5xkd2EeTtuR0d8znZDTHw1iZmaF+JSUmZkV4oZhZmaFuGGYmVkhbhhmZlaIG4aZmRXihmFmZoW4YZiZWSH/AjEjCigX8sm2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "s = fig.add_subplot(111)\n",
    "s.plot(arr[1570:2100,460], linewidth=0.6, color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('Phonocardiograph.png', dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}